# Artificial Intelligence-Based Interactive Art Design under Neural Network Vision Valve

Authors: Yuqi Zhao
Journal: Journal of Sensors
 (2022)

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## Abstract

Interactive art design (IAD) is an organic integration of art and technology. From the perspective of the development of AI machines from ancient times to the present, it has gone through the stages of command interface, graphical interface, and multimedia interface. The development of interactive art has been around for some years. As an art form, it not only brings optimization and enjoyment to people’s quality of life but also meets the needs of human-computer interaction, improves the efficiency of art design, and realizes human-human interaction. The purpose is to bring different feelings and experiences to the works and people’s psychology. In particular, the application of AI in IAD has not only brought great changes to designers. It also derives the interaction of behavioral limbs, which brings a greater experience and interaction to the audience, thereby creating a better interactive effect. Therefore, this paper completes the following work: (1) the research progress of AI in IAD at home and abroad is introduced. (2) The combination of AI and IAD is proposed, and the basic principle of RBF neural network and intelligent optimization algorithm are introduced, and the evaluation index of IAD is constructed. (3) Using the constructed dataset to test two intelligent optimization algorithms, the results show that the PSO-RBF model is more excellent in evaluating the quality of IAD. The trained model is used for experiments, and the output of the model is compared with the expert evaluation results, and the error is very small. Comparing the quality indicators of IAD before and after the integration of AI, the results show that AI has an excellent improvement effect on IAD.


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## Body

## 1. Introduction

The evolution of manufacturing equipment and production procedures has played a significant role in human civilization’s growth throughout thousands of years. Future technological advances in the form of the fourth industrial revolution will be another major step forward in human civilization’s progress toward a more intelligent society. Now is the moment for new generation information technology, new energy, and new transportation technology to take center stage in human civilizational evolution and change [1–3]. Intelligent technology will lead to productivity improvements and breakthroughs in a wide range of sectors, including IAD, as it develops in a steady and consistent manner. The popularity of artificial intelligence (AI) has risen to an all-time high, and many multinational corporations, both at home and abroad, are ready to spend significant sums of money to acquire top personnel to investigate different applications of AI. As far as big data is concerned, China is a major nation on the Internet that generates a lot of data every day, which makes it a better place to train AI algorithms than other countries [4, 5]. AI is now being researched to differing degrees in several domains, and the field of art design is an essential foundation for the use of AI technology, particularly in IAD [6, 7]. Google released the intelligent voice home assistant Google Home and announced that future development will be dominated by AI. Alibaba’s AI design platform “Luban” completed the design of 400 million posters on “Double Eleven”, reaching 8,000 designs per second. In 2017, during the “two sessions” in China, government departments wrote AI for the first time in their work reports, and research on AI has become a strategic policy of the country. During China’s “two sessions” in 2018, AI was mentioned again, which fully shows that AI has been valued and supported by the country today [8, 9]. In today’s advocating people-oriented and paying attention to humanistic care, the intervention of intelligent technology in IAD is undoubtedly a key factor for human beings to move towards humanistic care. The center of interaction is to create meaningful experience, and its essence is the interaction and communication between people and works. To effectively integrate AI with IAD, we must pay greater attention to the humanization of the interactive experience. Combining AI with IAD is precisely the research of today’s trending themes. The use of AI to IAD may affect not just the appearance of IAD but also whether it will be replaced by robots. The unknown is a psychological hazard [10–12]. For instance, many individuals fear that AI may replace their occupations. In actuality, the intervention of AI is a postintelligent period of art and design in which people and computers coevolve. The fast growth of AI has become an inescapable obstacle on the path to the advancement of human science and technology in the digital era. From smart city to smart home to smart design, artificial intelligence is advancing in every aspect of our lives [13, 14]. With the continuous pursuit of personal feelings and personalization, the efforts of artists and designers are not enough. It is also necessary to use technology to support the continuous development of personalized needs. AI is on the verge of a revolution in intelligence. With the fast advancement of technology, there are an increasing number of AI applications in IAD. With the fast advancement of computer technology and the Internet [15], the interactive characteristics of creative design have grown more pronounced and unique. In an unprecedented manner, the computer as a control device governs the interaction process. On the basis of the present intelligent technology used in IAD, AI has also started to emerge in our everyday lives. Intelligent IAD may adapt to the recipient’s behaviour, posture, voice, expression, temperature, remote control, and climatic change through robotics, computers, sensors, intelligent programmes, intelligent materials, and other intelligent technologies [16, 17]. Combining IAD with AI extends the conventional creative form. Intelligent technology’s participation has brought forth new issues, but it has also provided us with a fresh viewpoint from which to reconsider art. This paper uses the research hotspot of AI to cut into the IAD and conducts research on the developing intelligent IAD. The neural network is then utilised to assess the quality of the interface design after the incorporation of artificial intelligence, providing a strong academic theoretical foundation for AI in the area of IAD.The unique contribution of the paper includes the following:(i)
Implementation of AI in interactive art design(ii)
Development of a framework using RBF algorithm and PSO for optimization(iii)
Evaluation of the PSO-RBF model in comparison to the state-of-the-art approaches


## 2. Related Work

Domestic research on AI art design has only started in recent years, and a systematic research method has not yet been formed. It mainly tends to study the art form of a specific field of AI. Reference [18] begins with a detailed discussion of what is singularity technology and singularity art and the relationship between technology and art in the context of the development of singularity technology. It systematically introduces the changes brought by intelligent technology and intelligent materials to traditional art design. This paper discusses in detail how the current artists use AI technologies such as intelligent robots, intelligent interaction, and virtual reality to realize art design creation and the impact of the development of intelligent materials on art design in the future. It boldly speculates and reasonably deduces the transformation of future art under the impact of singularity. With the fast development of AI technology, reference [19] describes in detail how virtual reality technology might be artistically merged with art design. It examines the link between science, art, and aesthetics, as well as the ideas and practises associated with mixing virtual reality technology with art design. Based on virtual reality technology and under the guidance of virtual art design, the author builds the primary content of virtual reality art design research in collaboration. By taking virtual reality art design as an entry point, we can grasp the research paradigm of digital art design macroscopically. According to reference [20], modern art design and virtual reality technology may now be integrated in a new way. The research is carried out from the research objects, research tasks, realization means and artistic characteristics of virtual reality art design, etc., respectively. It is concluded that virtual reality technology will have a big influence on art design in the future because of the development of AI. Reference [21] discusses the impact of the development of AI technology on art design and redefines the new relationship between art and technology from the perspective of AI development and discusses the potential of AI art in terms of the value of artists and works of art. Reference [22] believes that art is a creative activity carried out by human beings through free will, and AI only plays a role in sharing part of human labor in this process. There is a fundamental difference between this kind of labor and the creative activities of human beings in artistic creation, that is, whether there is free will or not, and all creations performed before AI has free will cannot be called artistic design. Reference [23] systematically discusses the relationship between art and science, as well as the research content of virtual reality art design. Reference [24] briefly introduces the impact of AI, virtual reality, and other high-tech technologies on the traditional interactive mode of art design and expounds the influence of subjective and objective factors on the development of IAD from the aspects of sensory, media, and aesthetic characteristics. Reference [25] gave a detailed introduction to the penetration of AI, virtual reality, holographic projection, robots, and other high-tech technologies into art design by means of exhibition cases. Reference [26] systematically discusses virtual reality art design. The author describes the immersive artistic aesthetic experience and virtual and real space of virtual reality art design in the way of works appreciation. The western research on AI art design is earlier than China, and the research on AI art design has made certain achievements. Western scholars’ research on AI art is mainly carried out from two aspects: theory and art design practice. The growth of IAD, intelligent art design, and creative philosophy from 1964 to 2011 is detailed in reference [27]. Predicting how art design will evolve and how intelligent elements will influence it is also important. Reference [28] compares the design of contemporary new media art with those of historic virtual art from the viewpoints of artistic aesthetics and creative expression. There is a comparison between conventional and virtual reality art in reference [29]. Virtual art design has progressed from fantasy to immersive experience, as described in this piece. References [30, 31] discuss in detail the relationship between art design and AI through GAN, an art group obvious composed of three artists in Paris. Reference [32] imitates many works of art masters by reconstructing ANNs and using AI recognition technology. An impressive amount of work has been done by reference [33] in the area of artificial intelligence art design, particularly in the area of combining robots and humans and then controlling the creative production of human behaviour. Reference [34] uses AI pattern recognition technology to study the visual information recognition and image generation art of intelligent robots.


## 3. Method

### 3.1. Application of AI in IAD

(1)
IAD of intelligent platform: the first stage of development is IAD with intelligent platform as the main part. Taichung conducts human-computer interactive art creation. At this stage, AI already has certain natural language, machine vision, etc. By simulating works designed by human intelligence, technology is used to collect, analyze, model, and sort out massive data. When the designers need to create a certain work, the relevant data or information can be extracted from the database at the first stage. Then, the work can be deconstructed by the designer, and the required relevant work generated can be created with the help of a single click. For example, Alibaba’s Luban can design 8,000 posters in one second. Designing intelligent design platforms has become easier because of the advances in science and technology. In addition to Luban, there are also intelligent platforms related to design, which can implement automatic color matching through various matching methods, coloring, correction, and other functions, so as to provide more and more designers with convenient services. Among them, in terms of text matching text IAD, intelligent color matching is first collected, analyzed, and refined in the database. Then, the pixels on the image are captured, a reasonable algorithm is selected within the system, and different pixels are fed back in the form of text, and the final color matching model is formed for different color distribution areas to complete the work. Another example is the IAD of text matching images, the main principle of which is to automatically generate design proposals through design suggestions. According to different needs, AI can judge the excellent design works by collating massive data and comparing the data of the works of excellent designers(2)
IAD of intelligent machines: the second stage of development of artificial intelligence ushered in the intelligent machine-oriented mode and intelligent machines can complete the works required by users through simulation and cloning. Compared with the first stage, this stage is more artistic and expressive. In August 2015, German research experts announced the results of AI research, the main content of which is that the AI system is carrying out deep learning on the painting style of the world’s famous painters. Through the multilevel network structure, different levels of information in the image are extracted. For example, when learning a painting, AI will first select large color blocks with large color differences and then gradually deepen the color, pay attention to more painting details, and use the polyline to achieve the application purpose of the visual recognition function. At the same time, in the actual process of extracting various kinds of fine information, the AI machine will also filter unnecessary factors through its own software. Especially in IAD, AI machines have the best performance in painting design. The principle of its painting design is realized through neural network, image experience, and related main functions(3)
IAD of human-machine collaboration in intelligent technology: the third stage of AI development has ushered in the human-machine collaborative creation model. IAD with the help of human-machine collaboration mode allows users to get a whole-hearted immersive experience or create works together with designers. This form is also a mainstream way in the future. The essence of immersive synaesthesia experience is the art produced by the interaction of people, equipment, and space. It is a spatial interaction of people, machines, and interfaces. The word synaesthesia is derived from the wide variety of experiments conducted by artists who have explored the impact and cooperation of senses, namely, seeing and hearing. Synaesthesia is found in all forms of arts, namely, visual music, music visualization, audiovisual art, abstract film, and intermedia. Thus, a new artistic logic is formed, which can mobilize the various senses of the participants, help them better understand the information of the works, and give timely feedback. However, in this field with a common structure of space, the participants and the works can create a very strong sense of immersion through interaction. Virtual reality technology is such a product, and the difference between it and the real world is gradually narrowing with the advancement of technology, and the boundaries between the two are gradually overlapping, and it is even difficult to distinguish and define in some cases. The emergence of virtual reality technology not only has a great impact on people’s way of life but also gradually subverts the original way of human cognition. The continuous development of strong intelligent IAD has higher and higher requirements for designers. Intelligent interactive art is the art of responding to changes in the recipient’s behavior, posture, and voice through intelligent tools such as intelligent robots. Through such advanced technical equipment, designers can use language, expressions, etc. to create a variety of interactive artworks


### 3.2. The Structure and Characteristics of RBF Neural Network

RBFs have just one independent variable: their distance from the origin. The monotonic function of the Euclidean distance from any point in the space to the midway is sometimes referred to as the midpoint distance. For the RBF neural network, the hidden layer neurons are excited by radial basis functions, the input vector is transformed once, and the low-dimensional mode is converted to the high-dimensional mode before the output of the hidden layer neurons is weighted and summed. The cover theorem ensures the RBF neural network’s mathematical logic. For example, a nonlinear pattern classification issue is more linearly separable in high-dimensional space than in low-dimensional space, according to the theorem. Only one hidden layer exists in RBF neural networks, unlike other forward neural networks, which have several hidden layers. For example, there is no processing done by neurons in the input layer before transmitting data to the hidden layer neurons. This is a direct link. Radial basis functions, which may be characterized as nonlinear, nonnegative, radially symmetric decay functions, are used in the activation function of the hidden layer. The hidden layer is connected to the output layer through a linear weighted link. As a result, there is no connectivity between neurons inside a single layer of an RBF neural network. There are no local minima in an RBF neural network since it has an extremely basic topology. The output function of the RBF neural network can generally be expressed as(1)Fx=∑i=1nwiφx−ci,where c represents the center of the basis function, wi represents the weight, and φx−ci is a set of radial basis functions, so Fx is expressed as a set of radial basis functions for linearity fit.The commonly used radial basis functions are as follows:(1)
Gaussian function:(2)∅x,c=exp−x−c22σ2(2)
Anomalous sigmoid function:(3)∅x,c=11+expx−c2/σ2(3)
Fit the quadratic function:(4)∅x,c=11+x−c2+σ2wherein σ represents the width of the radial basis function. The smaller the σ, the smaller is the width of the radial basis function and the greater the selectivity of the radial basis functionThe Gaussian function is the most often used, and it offers the following benefits: the depiction is straightforward. When it is the same, the selectivity is greatest and the breadth is least. Because the general function may be represented as a linear combination of a number of basis functions, it has high smoothness and can take any derivative. RBF consists of two layers of processing wherein the first input is mapped into each RBF in the hidden layer. The function which is used normally is the Gaussian function, and its value depends on the distance to the center of the input space, similar to the concept of Euclidian distance. The weighted connection between the neurons in the hidden layer and the neurons in the output layer produces a linear combination according to the approximation principle of the RBF neural network. The RBF neural network has only one hidden layer which is also known as the feature vector. The RBF neural network features the following: in the RBF neural network, there is just one hidden layer and a basic structure. Only output data is sent to hidden layer neurons in the RBF neural network’s input layer. There is no further processing. The radial basis function, a local function, activates the buried layer neurons in the RBF neural network. Only if the input data is in a small region will the function produce a meaningful response; i.e., the output will be nonzero. Transform the global optimal problem into a linear summation of local optima. The main advantage of using RBF is that it uses only one hidden layer and radial basis function is used as activation function which help in approximation. The model is easily designable and has good generalization and strong tolerance towards input noise and online learning ability.


### 3.3. Comparison of RBF Neural Network and BP Neural Network

Both RBFNN and BPNN are nonlinear multilayer feedforward neural networks, and they are both general-purpose approximators. From a certain point of view, the two are the same, because for any BPNN, an RBF neural network can be found to replace it and vice versa. But there are many differences between the two, as follows:(1)
RBFNN only uses weighted connections between the hidden layer and output layer, direct connections between the input layer and the hidden layer, and weighted connections between all the layers of BPNN, from a network structure viewpoint. A nonlinear excitation function is used by the hidden layer neurons of the BPNN in comparison to a Gaussian excitation function by the hidden layer neurons of the RBF neural network. The number of hidden layers and neurons in the hidden layer of the BPNN are unknown. The number of hidden layers and the number of neurons in the hidden layer cannot be modified after the network model has been established. When a certain issue requires more or fewer neurons in a hidden layer, an RBF neural network has just one hidden layer(2)
From the perspective of the training algorithm, the BPNN adopts the gradient descent method, that is, starting from a certain starting point, training samples in the direction of error reduction, so that the error reaches the minimum value. However, for practical problems in reality, the network is more complex, and the error function is a curved surface in a multidimensional space. The error is easy to fall into a local minimum value of the curved surface. Therefore, the movement of the point in all directions will lead to an increase in the error, so that the error will fall into local minima rather than global minima. Because it is hidden inside an infinite range of space, this function is always nonzero in the input space used by the BPNN hidden layer. As a global approximation neural network, the weights of the whole network must be adjusted during each training session. The rate of training is rather sluggish. A small subset of the input space is used by the RBF neural network’s hidden layer, which employs radial basis functions with nonzero values. It is a local approximation neural network, so it does not get trapped in local minima, and the RBF neural network converges fast(3)
From the perspective of approximation ability, theoretically, both RBF neural network and BPNN can approximate any nonlinear system with arbitrary precision. Due to the different excitation functions used, the approximation performance is also different. The convergence speed of RBF neural network is faster than that of BPNN, and the topology of RBF neural network is clear. It has good approximation ability for nonlinear systems, and RBF neural network has become the main model in many fields because of its stronger vitality


### 3.4. Intelligent Optimization Algorithm

#### 3.4.1. Genetic Algorithms

Genetic algorithm (GA) can be mainly divided into the following processes:(1)
Coding: the parameters of the issue space cannot be directly dealt with by the GA. In order for it to be used, it must be transformed into a genetic code string based on a certain coding technique, which is an individual. This conversion process is called coding. The problem to be solved by coding is to express the candidate solutions in the population with a simple and practical genetic code string. The encoding method also affects the performance of the genetic algorithm. There are two commonly used encoding methods. The binary encoding method converts the parameters to be solved in the original problem into binary form; that is, the encoded symbols are only represented by binary symbols 0 and 1. The binary coding method conforms to the structure of chromosomes, the operations of crossover and mutation are simple, and there are many algorithm modes. However, since the gene length needs to be determined first in the solution process, the precision of the parameters is also limited, and the precision cannot be changed during the solution process. And the individual length of the binary coding method is long, so the solution efficiency is low. It is possible to encode an individual’s genetic information using a floating-point encoding approach, which implies that each gene is represented by the original value of the parameter, which is an integer number in a certain range. Solution accuracy and efficiency are both enhanced by using floating-point encoding(2)
Generating a population: the first step in generating a population is to define the number of individuals, that is, the population size. When the population size is large, it is easy to find the optimal solution, but the calculation time of the algorithm will be prolonged. Reducing the population size can shorten the calculation time, but it is prone to premature maturation. The second step is to randomly generate gene strings for each chromosome(3)
The fitness function, which is used in the genetic algorithm to represent the quality of the individual’s  location: when a person has a higher fitness level, they are closer to finding the best answer, increasing the likelihood that their traits will be passed down to future generations. As a result, those who are less fit will have a decreased chance of passing on their traits to the next generation, in keeping with the idea of survival of the fittest. Fitness function construction has two requirements: the fitness function’s value cannot be zero, and the fitness function’s rise must be compatible with the optimization of the objective function, which means it must increase in a direction consistent with that function’s optimization(4)
The selection operation is to select suitable individuals to reproduce offspring on the basis of the fitness evaluation of individuals in the population. People who are more physically fit are more likely to be chosen, whereas those who are less physically fit are less likely to be chosen. The most commonly used selection method is the wheel selection method, which takes the sum of the fitness of all individuals as a roulette wheel. The fitness of each individual corresponds to a part of the area in the roulette, and the greater the fitness, the larger the area occupied by the individual. When the wheel is rotated, the position of the pointer is the selected individual(5)
Crossover operation, that is, the main parts of the two parent individuals are exchanged in a certain way, thereby forming two new individuals: crossover operation is the main feature that distinguishes the genetic algorithm from other algorithms, and the crossover operation affects the global search ability of the entire genetic algorithm. There are different crossover algorithms for different encoding methods(6)
Mutation operation, that is, to change some gene positions of some individuals in the population: according to the different coding methods, it is divided into real-valued variation and binary variation. The mutation operation generally has two processes. The first process is to judge whether the individual needs to be mutated according to the preset mutation probability. The second process is to select random locations for mutation of individuals that need to be mutated. It is possible to boost the global search capabilities of the algorithm by incorporating mutation in the early stages of an iteration in order to assure the multidirectionality of population members and avoid premature phenomena. Secondly, we want to improve the algorithm’s capacity to quickly find the ideal solution for each person in the latter stages of the iteration. Obviously, in the early stage of iteration, a small mutation probability cannot guarantee the multidirectionality of the population. In the later stage of iteration, individuals are concentrated in the neighborhood of the optimal solution, and a large mutation probability will destroy the optimal solution gene. As a result, the mutation probability should be higher in the early stages of iteration and lower in the latter stages, as explained above


#### 3.4.2. The Principle of Particle Swarm Optimization Algorithm

From the modelling of birds’ social systems, the development of PSO (particle swarm optimization) was born. Consider a scenario in which a flock of birds is out foraging in an area where there is only one food source and no one in the flock knows where it is. Knowing the distance to the food, the easiest technique to locate the food is to approach the bird closest to the meal and scan the area surrounding that bird. A flock of birds is the starting point for PSO’s social simulation, and each of the birds is referred to as a particle. Using the objective function, each particle has a fitness value that is based on its present location and the best place it has previously looked for. An iteration of this process occurs in which the particle flies at a predetermined speed, which is determined by a fitness function value and the particle’s flying direction and distance. Particle speed is governed by “two extreme values”: the “individual extreme value” or pBest, which is the particle’s best possible location in the iterative process. Iteratively searching for the best possible location, known as gBest, is the other extreme value, which is referred to as the group extreme value or gBest. Set the search space to be D-dimensional and the number of particles toN for the conventional PSO algorithm’s mathematical formulation. The position of the ith particle is expressed as Xi=xi1,xi2,⋯,xiD; the optimal solution searched by the ith particle in the iterative process is denoted as pi=pi1,pi2,⋯,piD, that is, individual extrema pBest. The optimal solution in the individual extreme value pi searched by all particles is recorded as pg, which is the group extreme value gBest. The flying speed of the ith particle is the vector Vi=vi1,vi2,⋯,viD. Then, the D-dimensional flight speed of each particle is expressed as
(5)vidt+1=wvidt+c1randpidt−xidt+c2randpgdt−xidt,(6)xidt+1=xidt+vidt+1,where i=1,2,⋯,N, d=1,2,⋯,D, acceleration factors c1 and c2 are normal numbers, rand is a random number of [0, 1], w is called inertia factor, xidt is the current position of the ith particle, vidt is the current velocity of the ith particle, pidt is the optimal solution in the historical solution of the ith particle, and pgdt is the entire particle swarm the optimal solution.In the iterative process, the moving distance and moving speed of the ions need to be limited. The position change and speed change range of thedth dimension are −xd,max,xd,max and −vd,max,vd,max, respectively. If the moving distance or moving speed of the particles exceeds the maximum variation range during the iteration, the boundary of the maximum variation range is taken. The main advantages of using PSO are its simplicity of use, robustness in controlling the parameters, and its ability to achieve optimal efficiency in comparison to other mathematical algorithms. PSO is a metaheuristic technique which does not make any assumptions about the problem which is being optimized, and it can be easily parallelized for the purpose of concurrent processing.Various studies have been conducted using PSO in arts and related domain. As an example, the study in [35] analyzed the basic concepts of art therapy and children’s painting using PSO technique. The study was conducted in preschool for art teaching among children. The study focused on finding the global optimal fitness function which reduced the computational complexity and also provided maximum coverage to the existing network. The study in [36] performed application analysis of fusion particle swarm optimization for the performance evaluation of the instructors in the academic domain. The PSO and fuzzy comprehensive evaluation was performed for evaluating the instructors’ performance, and an index parameter was used between a scale of 2.5 and 3.0 which indicated that the performance was excellent.


### 3.5. IAD Evaluation Index System

According to the AI-based IAD framework proposed in this paper, an IAD evaluation index system is constructed, as shown in Table1.Table 1
AI IAD evaluation index system.
IndexLabelProduct structural strengthL1Environmentally friendly interior materialsL2Link part safetyL3Efficient packagingL4Beautiful surface decorationL5Good visual experienceL6Sanitary details are in placeL7Safe for extreme useL8Potential hazard controlL9Reasonable functionL10Easy to transportL11Reasonable roundness of shapeL12Smooth interactionL13Comfortable interface designL14High scalabilityL15Material seams smoothL16Environmentally friendly packaging materialsL17High functional innovationL18


## 3.1. Application of AI in IAD

(1)
IAD of intelligent platform: the first stage of development is IAD with intelligent platform as the main part. Taichung conducts human-computer interactive art creation. At this stage, AI already has certain natural language, machine vision, etc. By simulating works designed by human intelligence, technology is used to collect, analyze, model, and sort out massive data. When the designers need to create a certain work, the relevant data or information can be extracted from the database at the first stage. Then, the work can be deconstructed by the designer, and the required relevant work generated can be created with the help of a single click. For example, Alibaba’s Luban can design 8,000 posters in one second. Designing intelligent design platforms has become easier because of the advances in science and technology. In addition to Luban, there are also intelligent platforms related to design, which can implement automatic color matching through various matching methods, coloring, correction, and other functions, so as to provide more and more designers with convenient services. Among them, in terms of text matching text IAD, intelligent color matching is first collected, analyzed, and refined in the database. Then, the pixels on the image are captured, a reasonable algorithm is selected within the system, and different pixels are fed back in the form of text, and the final color matching model is formed for different color distribution areas to complete the work. Another example is the IAD of text matching images, the main principle of which is to automatically generate design proposals through design suggestions. According to different needs, AI can judge the excellent design works by collating massive data and comparing the data of the works of excellent designers(2)
IAD of intelligent machines: the second stage of development of artificial intelligence ushered in the intelligent machine-oriented mode and intelligent machines can complete the works required by users through simulation and cloning. Compared with the first stage, this stage is more artistic and expressive. In August 2015, German research experts announced the results of AI research, the main content of which is that the AI system is carrying out deep learning on the painting style of the world’s famous painters. Through the multilevel network structure, different levels of information in the image are extracted. For example, when learning a painting, AI will first select large color blocks with large color differences and then gradually deepen the color, pay attention to more painting details, and use the polyline to achieve the application purpose of the visual recognition function. At the same time, in the actual process of extracting various kinds of fine information, the AI machine will also filter unnecessary factors through its own software. Especially in IAD, AI machines have the best performance in painting design. The principle of its painting design is realized through neural network, image experience, and related main functions(3)
IAD of human-machine collaboration in intelligent technology: the third stage of AI development has ushered in the human-machine collaborative creation model. IAD with the help of human-machine collaboration mode allows users to get a whole-hearted immersive experience or create works together with designers. This form is also a mainstream way in the future. The essence of immersive synaesthesia experience is the art produced by the interaction of people, equipment, and space. It is a spatial interaction of people, machines, and interfaces. The word synaesthesia is derived from the wide variety of experiments conducted by artists who have explored the impact and cooperation of senses, namely, seeing and hearing. Synaesthesia is found in all forms of arts, namely, visual music, music visualization, audiovisual art, abstract film, and intermedia. Thus, a new artistic logic is formed, which can mobilize the various senses of the participants, help them better understand the information of the works, and give timely feedback. However, in this field with a common structure of space, the participants and the works can create a very strong sense of immersion through interaction. Virtual reality technology is such a product, and the difference between it and the real world is gradually narrowing with the advancement of technology, and the boundaries between the two are gradually overlapping, and it is even difficult to distinguish and define in some cases. The emergence of virtual reality technology not only has a great impact on people’s way of life but also gradually subverts the original way of human cognition. The continuous development of strong intelligent IAD has higher and higher requirements for designers. Intelligent interactive art is the art of responding to changes in the recipient’s behavior, posture, and voice through intelligent tools such as intelligent robots. Through such advanced technical equipment, designers can use language, expressions, etc. to create a variety of interactive artworks


## 3.2. The Structure and Characteristics of RBF Neural Network

RBFs have just one independent variable: their distance from the origin. The monotonic function of the Euclidean distance from any point in the space to the midway is sometimes referred to as the midpoint distance. For the RBF neural network, the hidden layer neurons are excited by radial basis functions, the input vector is transformed once, and the low-dimensional mode is converted to the high-dimensional mode before the output of the hidden layer neurons is weighted and summed. The cover theorem ensures the RBF neural network’s mathematical logic. For example, a nonlinear pattern classification issue is more linearly separable in high-dimensional space than in low-dimensional space, according to the theorem. Only one hidden layer exists in RBF neural networks, unlike other forward neural networks, which have several hidden layers. For example, there is no processing done by neurons in the input layer before transmitting data to the hidden layer neurons. This is a direct link. Radial basis functions, which may be characterized as nonlinear, nonnegative, radially symmetric decay functions, are used in the activation function of the hidden layer. The hidden layer is connected to the output layer through a linear weighted link. As a result, there is no connectivity between neurons inside a single layer of an RBF neural network. There are no local minima in an RBF neural network since it has an extremely basic topology. The output function of the RBF neural network can generally be expressed as(1)Fx=∑i=1nwiφx−ci,where c represents the center of the basis function, wi represents the weight, and φx−ci is a set of radial basis functions, so Fx is expressed as a set of radial basis functions for linearity fit.The commonly used radial basis functions are as follows:(1)
Gaussian function:(2)∅x,c=exp−x−c22σ2(2)
Anomalous sigmoid function:(3)∅x,c=11+expx−c2/σ2(3)
Fit the quadratic function:(4)∅x,c=11+x−c2+σ2wherein σ represents the width of the radial basis function. The smaller the σ, the smaller is the width of the radial basis function and the greater the selectivity of the radial basis functionThe Gaussian function is the most often used, and it offers the following benefits: the depiction is straightforward. When it is the same, the selectivity is greatest and the breadth is least. Because the general function may be represented as a linear combination of a number of basis functions, it has high smoothness and can take any derivative. RBF consists of two layers of processing wherein the first input is mapped into each RBF in the hidden layer. The function which is used normally is the Gaussian function, and its value depends on the distance to the center of the input space, similar to the concept of Euclidian distance. The weighted connection between the neurons in the hidden layer and the neurons in the output layer produces a linear combination according to the approximation principle of the RBF neural network. The RBF neural network has only one hidden layer which is also known as the feature vector. The RBF neural network features the following: in the RBF neural network, there is just one hidden layer and a basic structure. Only output data is sent to hidden layer neurons in the RBF neural network’s input layer. There is no further processing. The radial basis function, a local function, activates the buried layer neurons in the RBF neural network. Only if the input data is in a small region will the function produce a meaningful response; i.e., the output will be nonzero. Transform the global optimal problem into a linear summation of local optima. The main advantage of using RBF is that it uses only one hidden layer and radial basis function is used as activation function which help in approximation. The model is easily designable and has good generalization and strong tolerance towards input noise and online learning ability.


## 3.3. Comparison of RBF Neural Network and BP Neural Network

Both RBFNN and BPNN are nonlinear multilayer feedforward neural networks, and they are both general-purpose approximators. From a certain point of view, the two are the same, because for any BPNN, an RBF neural network can be found to replace it and vice versa. But there are many differences between the two, as follows:(1)
RBFNN only uses weighted connections between the hidden layer and output layer, direct connections between the input layer and the hidden layer, and weighted connections between all the layers of BPNN, from a network structure viewpoint. A nonlinear excitation function is used by the hidden layer neurons of the BPNN in comparison to a Gaussian excitation function by the hidden layer neurons of the RBF neural network. The number of hidden layers and neurons in the hidden layer of the BPNN are unknown. The number of hidden layers and the number of neurons in the hidden layer cannot be modified after the network model has been established. When a certain issue requires more or fewer neurons in a hidden layer, an RBF neural network has just one hidden layer(2)
From the perspective of the training algorithm, the BPNN adopts the gradient descent method, that is, starting from a certain starting point, training samples in the direction of error reduction, so that the error reaches the minimum value. However, for practical problems in reality, the network is more complex, and the error function is a curved surface in a multidimensional space. The error is easy to fall into a local minimum value of the curved surface. Therefore, the movement of the point in all directions will lead to an increase in the error, so that the error will fall into local minima rather than global minima. Because it is hidden inside an infinite range of space, this function is always nonzero in the input space used by the BPNN hidden layer. As a global approximation neural network, the weights of the whole network must be adjusted during each training session. The rate of training is rather sluggish. A small subset of the input space is used by the RBF neural network’s hidden layer, which employs radial basis functions with nonzero values. It is a local approximation neural network, so it does not get trapped in local minima, and the RBF neural network converges fast(3)
From the perspective of approximation ability, theoretically, both RBF neural network and BPNN can approximate any nonlinear system with arbitrary precision. Due to the different excitation functions used, the approximation performance is also different. The convergence speed of RBF neural network is faster than that of BPNN, and the topology of RBF neural network is clear. It has good approximation ability for nonlinear systems, and RBF neural network has become the main model in many fields because of its stronger vitality


## 3.4. Intelligent Optimization Algorithm

### 3.4.1. Genetic Algorithms

Genetic algorithm (GA) can be mainly divided into the following processes:(1)
Coding: the parameters of the issue space cannot be directly dealt with by the GA. In order for it to be used, it must be transformed into a genetic code string based on a certain coding technique, which is an individual. This conversion process is called coding. The problem to be solved by coding is to express the candidate solutions in the population with a simple and practical genetic code string. The encoding method also affects the performance of the genetic algorithm. There are two commonly used encoding methods. The binary encoding method converts the parameters to be solved in the original problem into binary form; that is, the encoded symbols are only represented by binary symbols 0 and 1. The binary coding method conforms to the structure of chromosomes, the operations of crossover and mutation are simple, and there are many algorithm modes. However, since the gene length needs to be determined first in the solution process, the precision of the parameters is also limited, and the precision cannot be changed during the solution process. And the individual length of the binary coding method is long, so the solution efficiency is low. It is possible to encode an individual’s genetic information using a floating-point encoding approach, which implies that each gene is represented by the original value of the parameter, which is an integer number in a certain range. Solution accuracy and efficiency are both enhanced by using floating-point encoding(2)
Generating a population: the first step in generating a population is to define the number of individuals, that is, the population size. When the population size is large, it is easy to find the optimal solution, but the calculation time of the algorithm will be prolonged. Reducing the population size can shorten the calculation time, but it is prone to premature maturation. The second step is to randomly generate gene strings for each chromosome(3)
The fitness function, which is used in the genetic algorithm to represent the quality of the individual’s  location: when a person has a higher fitness level, they are closer to finding the best answer, increasing the likelihood that their traits will be passed down to future generations. As a result, those who are less fit will have a decreased chance of passing on their traits to the next generation, in keeping with the idea of survival of the fittest. Fitness function construction has two requirements: the fitness function’s value cannot be zero, and the fitness function’s rise must be compatible with the optimization of the objective function, which means it must increase in a direction consistent with that function’s optimization(4)
The selection operation is to select suitable individuals to reproduce offspring on the basis of the fitness evaluation of individuals in the population. People who are more physically fit are more likely to be chosen, whereas those who are less physically fit are less likely to be chosen. The most commonly used selection method is the wheel selection method, which takes the sum of the fitness of all individuals as a roulette wheel. The fitness of each individual corresponds to a part of the area in the roulette, and the greater the fitness, the larger the area occupied by the individual. When the wheel is rotated, the position of the pointer is the selected individual(5)
Crossover operation, that is, the main parts of the two parent individuals are exchanged in a certain way, thereby forming two new individuals: crossover operation is the main feature that distinguishes the genetic algorithm from other algorithms, and the crossover operation affects the global search ability of the entire genetic algorithm. There are different crossover algorithms for different encoding methods(6)
Mutation operation, that is, to change some gene positions of some individuals in the population: according to the different coding methods, it is divided into real-valued variation and binary variation. The mutation operation generally has two processes. The first process is to judge whether the individual needs to be mutated according to the preset mutation probability. The second process is to select random locations for mutation of individuals that need to be mutated. It is possible to boost the global search capabilities of the algorithm by incorporating mutation in the early stages of an iteration in order to assure the multidirectionality of population members and avoid premature phenomena. Secondly, we want to improve the algorithm’s capacity to quickly find the ideal solution for each person in the latter stages of the iteration. Obviously, in the early stage of iteration, a small mutation probability cannot guarantee the multidirectionality of the population. In the later stage of iteration, individuals are concentrated in the neighborhood of the optimal solution, and a large mutation probability will destroy the optimal solution gene. As a result, the mutation probability should be higher in the early stages of iteration and lower in the latter stages, as explained above


### 3.4.2. The Principle of Particle Swarm Optimization Algorithm

From the modelling of birds’ social systems, the development of PSO (particle swarm optimization) was born. Consider a scenario in which a flock of birds is out foraging in an area where there is only one food source and no one in the flock knows where it is. Knowing the distance to the food, the easiest technique to locate the food is to approach the bird closest to the meal and scan the area surrounding that bird. A flock of birds is the starting point for PSO’s social simulation, and each of the birds is referred to as a particle. Using the objective function, each particle has a fitness value that is based on its present location and the best place it has previously looked for. An iteration of this process occurs in which the particle flies at a predetermined speed, which is determined by a fitness function value and the particle’s flying direction and distance. Particle speed is governed by “two extreme values”: the “individual extreme value” or pBest, which is the particle’s best possible location in the iterative process. Iteratively searching for the best possible location, known as gBest, is the other extreme value, which is referred to as the group extreme value or gBest. Set the search space to be D-dimensional and the number of particles toN for the conventional PSO algorithm’s mathematical formulation. The position of the ith particle is expressed as Xi=xi1,xi2,⋯,xiD; the optimal solution searched by the ith particle in the iterative process is denoted as pi=pi1,pi2,⋯,piD, that is, individual extrema pBest. The optimal solution in the individual extreme value pi searched by all particles is recorded as pg, which is the group extreme value gBest. The flying speed of the ith particle is the vector Vi=vi1,vi2,⋯,viD. Then, the D-dimensional flight speed of each particle is expressed as
(5)vidt+1=wvidt+c1randpidt−xidt+c2randpgdt−xidt,(6)xidt+1=xidt+vidt+1,where i=1,2,⋯,N, d=1,2,⋯,D, acceleration factors c1 and c2 are normal numbers, rand is a random number of [0, 1], w is called inertia factor, xidt is the current position of the ith particle, vidt is the current velocity of the ith particle, pidt is the optimal solution in the historical solution of the ith particle, and pgdt is the entire particle swarm the optimal solution.In the iterative process, the moving distance and moving speed of the ions need to be limited. The position change and speed change range of thedth dimension are −xd,max,xd,max and −vd,max,vd,max, respectively. If the moving distance or moving speed of the particles exceeds the maximum variation range during the iteration, the boundary of the maximum variation range is taken. The main advantages of using PSO are its simplicity of use, robustness in controlling the parameters, and its ability to achieve optimal efficiency in comparison to other mathematical algorithms. PSO is a metaheuristic technique which does not make any assumptions about the problem which is being optimized, and it can be easily parallelized for the purpose of concurrent processing.Various studies have been conducted using PSO in arts and related domain. As an example, the study in [35] analyzed the basic concepts of art therapy and children’s painting using PSO technique. The study was conducted in preschool for art teaching among children. The study focused on finding the global optimal fitness function which reduced the computational complexity and also provided maximum coverage to the existing network. The study in [36] performed application analysis of fusion particle swarm optimization for the performance evaluation of the instructors in the academic domain. The PSO and fuzzy comprehensive evaluation was performed for evaluating the instructors’ performance, and an index parameter was used between a scale of 2.5 and 3.0 which indicated that the performance was excellent.


## 3.4.1. Genetic Algorithms

Genetic algorithm (GA) can be mainly divided into the following processes:(1)
Coding: the parameters of the issue space cannot be directly dealt with by the GA. In order for it to be used, it must be transformed into a genetic code string based on a certain coding technique, which is an individual. This conversion process is called coding. The problem to be solved by coding is to express the candidate solutions in the population with a simple and practical genetic code string. The encoding method also affects the performance of the genetic algorithm. There are two commonly used encoding methods. The binary encoding method converts the parameters to be solved in the original problem into binary form; that is, the encoded symbols are only represented by binary symbols 0 and 1. The binary coding method conforms to the structure of chromosomes, the operations of crossover and mutation are simple, and there are many algorithm modes. However, since the gene length needs to be determined first in the solution process, the precision of the parameters is also limited, and the precision cannot be changed during the solution process. And the individual length of the binary coding method is long, so the solution efficiency is low. It is possible to encode an individual’s genetic information using a floating-point encoding approach, which implies that each gene is represented by the original value of the parameter, which is an integer number in a certain range. Solution accuracy and efficiency are both enhanced by using floating-point encoding(2)
Generating a population: the first step in generating a population is to define the number of individuals, that is, the population size. When the population size is large, it is easy to find the optimal solution, but the calculation time of the algorithm will be prolonged. Reducing the population size can shorten the calculation time, but it is prone to premature maturation. The second step is to randomly generate gene strings for each chromosome(3)
The fitness function, which is used in the genetic algorithm to represent the quality of the individual’s  location: when a person has a higher fitness level, they are closer to finding the best answer, increasing the likelihood that their traits will be passed down to future generations. As a result, those who are less fit will have a decreased chance of passing on their traits to the next generation, in keeping with the idea of survival of the fittest. Fitness function construction has two requirements: the fitness function’s value cannot be zero, and the fitness function’s rise must be compatible with the optimization of the objective function, which means it must increase in a direction consistent with that function’s optimization(4)
The selection operation is to select suitable individuals to reproduce offspring on the basis of the fitness evaluation of individuals in the population. People who are more physically fit are more likely to be chosen, whereas those who are less physically fit are less likely to be chosen. The most commonly used selection method is the wheel selection method, which takes the sum of the fitness of all individuals as a roulette wheel. The fitness of each individual corresponds to a part of the area in the roulette, and the greater the fitness, the larger the area occupied by the individual. When the wheel is rotated, the position of the pointer is the selected individual(5)
Crossover operation, that is, the main parts of the two parent individuals are exchanged in a certain way, thereby forming two new individuals: crossover operation is the main feature that distinguishes the genetic algorithm from other algorithms, and the crossover operation affects the global search ability of the entire genetic algorithm. There are different crossover algorithms for different encoding methods(6)
Mutation operation, that is, to change some gene positions of some individuals in the population: according to the different coding methods, it is divided into real-valued variation and binary variation. The mutation operation generally has two processes. The first process is to judge whether the individual needs to be mutated according to the preset mutation probability. The second process is to select random locations for mutation of individuals that need to be mutated. It is possible to boost the global search capabilities of the algorithm by incorporating mutation in the early stages of an iteration in order to assure the multidirectionality of population members and avoid premature phenomena. Secondly, we want to improve the algorithm’s capacity to quickly find the ideal solution for each person in the latter stages of the iteration. Obviously, in the early stage of iteration, a small mutation probability cannot guarantee the multidirectionality of the population. In the later stage of iteration, individuals are concentrated in the neighborhood of the optimal solution, and a large mutation probability will destroy the optimal solution gene. As a result, the mutation probability should be higher in the early stages of iteration and lower in the latter stages, as explained above


## 3.4.2. The Principle of Particle Swarm Optimization Algorithm

From the modelling of birds’ social systems, the development of PSO (particle swarm optimization) was born. Consider a scenario in which a flock of birds is out foraging in an area where there is only one food source and no one in the flock knows where it is. Knowing the distance to the food, the easiest technique to locate the food is to approach the bird closest to the meal and scan the area surrounding that bird. A flock of birds is the starting point for PSO’s social simulation, and each of the birds is referred to as a particle. Using the objective function, each particle has a fitness value that is based on its present location and the best place it has previously looked for. An iteration of this process occurs in which the particle flies at a predetermined speed, which is determined by a fitness function value and the particle’s flying direction and distance. Particle speed is governed by “two extreme values”: the “individual extreme value” or pBest, which is the particle’s best possible location in the iterative process. Iteratively searching for the best possible location, known as gBest, is the other extreme value, which is referred to as the group extreme value or gBest. Set the search space to be D-dimensional and the number of particles toN for the conventional PSO algorithm’s mathematical formulation. The position of the ith particle is expressed as Xi=xi1,xi2,⋯,xiD; the optimal solution searched by the ith particle in the iterative process is denoted as pi=pi1,pi2,⋯,piD, that is, individual extrema pBest. The optimal solution in the individual extreme value pi searched by all particles is recorded as pg, which is the group extreme value gBest. The flying speed of the ith particle is the vector Vi=vi1,vi2,⋯,viD. Then, the D-dimensional flight speed of each particle is expressed as
(5)vidt+1=wvidt+c1randpidt−xidt+c2randpgdt−xidt,(6)xidt+1=xidt+vidt+1,where i=1,2,⋯,N, d=1,2,⋯,D, acceleration factors c1 and c2 are normal numbers, rand is a random number of [0, 1], w is called inertia factor, xidt is the current position of the ith particle, vidt is the current velocity of the ith particle, pidt is the optimal solution in the historical solution of the ith particle, and pgdt is the entire particle swarm the optimal solution.In the iterative process, the moving distance and moving speed of the ions need to be limited. The position change and speed change range of thedth dimension are −xd,max,xd,max and −vd,max,vd,max, respectively. If the moving distance or moving speed of the particles exceeds the maximum variation range during the iteration, the boundary of the maximum variation range is taken. The main advantages of using PSO are its simplicity of use, robustness in controlling the parameters, and its ability to achieve optimal efficiency in comparison to other mathematical algorithms. PSO is a metaheuristic technique which does not make any assumptions about the problem which is being optimized, and it can be easily parallelized for the purpose of concurrent processing.Various studies have been conducted using PSO in arts and related domain. As an example, the study in [35] analyzed the basic concepts of art therapy and children’s painting using PSO technique. The study was conducted in preschool for art teaching among children. The study focused on finding the global optimal fitness function which reduced the computational complexity and also provided maximum coverage to the existing network. The study in [36] performed application analysis of fusion particle swarm optimization for the performance evaluation of the instructors in the academic domain. The PSO and fuzzy comprehensive evaluation was performed for evaluating the instructors’ performance, and an index parameter was used between a scale of 2.5 and 3.0 which indicated that the performance was excellent.


## 3.5. IAD Evaluation Index System

According to the AI-based IAD framework proposed in this paper, an IAD evaluation index system is constructed, as shown in Table1.Table 1
AI IAD evaluation index system.
IndexLabelProduct structural strengthL1Environmentally friendly interior materialsL2Link part safetyL3Efficient packagingL4Beautiful surface decorationL5Good visual experienceL6Sanitary details are in placeL7Safe for extreme useL8Potential hazard controlL9Reasonable functionL10Easy to transportL11Reasonable roundness of shapeL12Smooth interactionL13Comfortable interface designL14High scalabilityL15Material seams smoothL16Environmentally friendly packaging materialsL17High functional innovationL18


## 4. Experiment and Analysis

### 4.1. Sample Data and Preprocessing

According to the IAD evaluation index system set in Chapter 3, this paper constructs a dataset for experimental testing, which includes 1600 sets of data. After selecting the experimental data, normalize the data, and normalize all the sample data to the interval [0,1], using the following formula:(7)x∗=x−xminxmax−xmin.


### 4.2. Network Optimization Parameter Selection

#### 4.2.1. RBF Neural Network Experiment Based on GA Optimization

In this paper, the crossover probability of the genetic algorithm is selected as 0.8, the mutation probability is 0.2, and the fitness value of the genetic algorithm when the number of neurons in the hidden layer of the neural network is 5-15 is shown in Figure1.Figure 1
Convergence of genetic algorithm with different number of hidden layer nodes.It can be seen that when the number of neurons in the hidden layer is 10, the convergence is the best. At this time, the optimal individual fitness curve in the genetic algorithm group is shown in Figure2.Figure 2
The optimal individual fitness curve of the genetic algorithm population.


#### 4.2.2. RBF Neural Network Experiment Based on PSO

In the particle swarm optimization algorithm in this paper, the acceleration factorc1=2 and c2=2 and the inertia factor adopt a linear decreasing weight strategy and select wmax=0.8 and wmin=0.4. The fitness value of the particle swarm algorithm when the number of hidden layer neurons in the working day neural network is 5-15 is shown in Figure 3.Figure 3
Convergence of PSO with different numbers of hidden layer nodes.When the number of neurons in the hidden layer is 14, its convergence performance is the best. At this time, the fitness value of the optimal individual in the iterative process of the PSO algorithm is shown in Figure4.Figure 4
The optimal individual fitness curve of the PSO population.It can be seen from the above experiments that the PSO algorithm has the smallest number of iterations and the largest fitness value. Compared with the genetic algorithm, the number of iterations is reduced by 26%, and the fitness value is increased by 16%. Therefore, from the overall performance point of view, the PSO algorithm has a faster convergence speed, and the searched optimal solution is more accurate.


### 4.3. Model Experiment Results

First, the PSO-RBF neural network model is trained, and the convergence of the model is obtained as shown in Figure5. It can be seen that the model quickly reaches a very low convergence value.Figure 5
PSO-RBF neural network model training effect.The trained neural network is used to evaluate the nontraining data in the sample data, and the prediction results of the PSO-RBF model are shown in Table2. It can be seen that the output value of the model is very close to the evaluation results of experts, indicating that the model has superior performance in evaluating the quality of IAD.Table 2
Experimental comparison between model output and expert evaluation results.
Number12345678Model output0.7520.7190.8260.6380.6560.9310.8730.775Expert results0.7530.7200.8260.6380.6570.9300.8700.774


### 4.4. The Effect of IAD Based on AI

In order to verify the effect of AI-based IAD proposed in this paper, the quality indicators of IAD before and after the integration of AI are compared, as shown in Figure6.Figure 6
Comparison of IAD quality before and after the integration of AI.


## 4.1. Sample Data and Preprocessing

According to the IAD evaluation index system set in Chapter 3, this paper constructs a dataset for experimental testing, which includes 1600 sets of data. After selecting the experimental data, normalize the data, and normalize all the sample data to the interval [0,1], using the following formula:(7)x∗=x−xminxmax−xmin.


## 4.2. Network Optimization Parameter Selection

### 4.2.1. RBF Neural Network Experiment Based on GA Optimization

In this paper, the crossover probability of the genetic algorithm is selected as 0.8, the mutation probability is 0.2, and the fitness value of the genetic algorithm when the number of neurons in the hidden layer of the neural network is 5-15 is shown in Figure1.Figure 1
Convergence of genetic algorithm with different number of hidden layer nodes.It can be seen that when the number of neurons in the hidden layer is 10, the convergence is the best. At this time, the optimal individual fitness curve in the genetic algorithm group is shown in Figure2.Figure 2
The optimal individual fitness curve of the genetic algorithm population.


### 4.2.2. RBF Neural Network Experiment Based on PSO

In the particle swarm optimization algorithm in this paper, the acceleration factorc1=2 and c2=2 and the inertia factor adopt a linear decreasing weight strategy and select wmax=0.8 and wmin=0.4. The fitness value of the particle swarm algorithm when the number of hidden layer neurons in the working day neural network is 5-15 is shown in Figure 3.Figure 3
Convergence of PSO with different numbers of hidden layer nodes.When the number of neurons in the hidden layer is 14, its convergence performance is the best. At this time, the fitness value of the optimal individual in the iterative process of the PSO algorithm is shown in Figure4.Figure 4
The optimal individual fitness curve of the PSO population.It can be seen from the above experiments that the PSO algorithm has the smallest number of iterations and the largest fitness value. Compared with the genetic algorithm, the number of iterations is reduced by 26%, and the fitness value is increased by 16%. Therefore, from the overall performance point of view, the PSO algorithm has a faster convergence speed, and the searched optimal solution is more accurate.


## 4.2.1. RBF Neural Network Experiment Based on GA Optimization

In this paper, the crossover probability of the genetic algorithm is selected as 0.8, the mutation probability is 0.2, and the fitness value of the genetic algorithm when the number of neurons in the hidden layer of the neural network is 5-15 is shown in Figure1.Figure 1
Convergence of genetic algorithm with different number of hidden layer nodes.It can be seen that when the number of neurons in the hidden layer is 10, the convergence is the best. At this time, the optimal individual fitness curve in the genetic algorithm group is shown in Figure2.Figure 2
The optimal individual fitness curve of the genetic algorithm population.


## 4.2.2. RBF Neural Network Experiment Based on PSO

In the particle swarm optimization algorithm in this paper, the acceleration factorc1=2 and c2=2 and the inertia factor adopt a linear decreasing weight strategy and select wmax=0.8 and wmin=0.4. The fitness value of the particle swarm algorithm when the number of hidden layer neurons in the working day neural network is 5-15 is shown in Figure 3.Figure 3
Convergence of PSO with different numbers of hidden layer nodes.When the number of neurons in the hidden layer is 14, its convergence performance is the best. At this time, the fitness value of the optimal individual in the iterative process of the PSO algorithm is shown in Figure4.Figure 4
The optimal individual fitness curve of the PSO population.It can be seen from the above experiments that the PSO algorithm has the smallest number of iterations and the largest fitness value. Compared with the genetic algorithm, the number of iterations is reduced by 26%, and the fitness value is increased by 16%. Therefore, from the overall performance point of view, the PSO algorithm has a faster convergence speed, and the searched optimal solution is more accurate.


## 4.3. Model Experiment Results

First, the PSO-RBF neural network model is trained, and the convergence of the model is obtained as shown in Figure5. It can be seen that the model quickly reaches a very low convergence value.Figure 5
PSO-RBF neural network model training effect.The trained neural network is used to evaluate the nontraining data in the sample data, and the prediction results of the PSO-RBF model are shown in Table2. It can be seen that the output value of the model is very close to the evaluation results of experts, indicating that the model has superior performance in evaluating the quality of IAD.Table 2
Experimental comparison between model output and expert evaluation results.
Number12345678Model output0.7520.7190.8260.6380.6560.9310.8730.775Expert results0.7530.7200.8260.6380.6570.9300.8700.774


## 4.4. The Effect of IAD Based on AI

In order to verify the effect of AI-based IAD proposed in this paper, the quality indicators of IAD before and after the integration of AI are compared, as shown in Figure6.Figure 6
Comparison of IAD quality before and after the integration of AI.


## 5. Conclusion

The application of AI in IAD will not only generate new application paradigms but also make the two promote each other. When discussing AI and IAD, it becomes clear that AI has a significant place in the field. Designers’ visual thinking is reconstructed and the audience’s perspective and experience are profoundly altered as a result. Using IAD as a new art form,  artists have sought to better serve their audiences’ emotional and experiential needs while also enhancing their own spiritual selves. Therefore, this paper has completed the following work: (1) in-depth analysis mainly focuses on the research of AI in IAD at home and abroad and expounds the help and significance of AI in IAD in the current digital age. The following  thesis lays the theoretical foundation. (2) The combination of AI and interaction design is proposed, and then, the basic  principle of RBF neural network and intelligent optimization algorithm are introduced, and the evaluation index of interaction design art is constructed. (3) Using the constructed dataset to test two intelligent optimization algorithms, the results show that the PSO-RBF model is more excellent in evaluating the artistic quality of interaction design. The trained model is used for experiments, and the output of the model is compared with the expert evaluation results, and the error is very small. Comparing the quality indicators of IAD before and after the integration of AI, the results show that AI has an excellent improvement effect on IAD. The results generated by the PSO-RBF model are promising, but the metrics used for evaluation are confined to convergence value, fitness value, and no. of iterations at convergence. The superiority of the model could be further justified by including accuracy, specificity, sensitivity, precision, and recall. This could be considered as future scope of research and observe how the model functions when such metrics are used for evaluation.



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*Source: 3628955-2022-09-25.xml*