# Visibility of Multisensor Digital Fusion Technology in Public Art Design under Complex Environments

Authors: Guang Chen
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Application of multisensor digital fusion technology in public art design in complex environments is deliberated to make public art design develop better. First, the more advanced sensor fusion technology is introduced into public art design, and the basic principle and system composition of multisensor fusion technology are analysed. The application of multisensor fusion technology in the field of public art is deeply analysed from multiple angles. Second, the visibility algorithm in public art design based on a fuzzy neural network (FNN) is studied, and the corresponding model is proposed. Finally, the proposed model is tested. The test results show that the root mean squared error of the model is 0.0261, the network has a good fitting effect on the output value, the similarity between the model output value and the real value is high, the fitting effect is good, the model is accurate and effective, and the identification accuracy is achieved. Moreover, the corresponding example model is proposed. The visibility development level of public art design in a region in the next 14 years is predicted. The algorithm proposed provides some ideas for the application of multisensor digital fusion technology in public art design.


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## Body

## 1. Introduction

Nowadays, the field of design has great changes. The link between science and technology and art culture connects the past and present design. Today, computer technology is increasingly important in art design. In a sense, high-tech art is used more frequently in life. Gradually, sensing technology has entered people’s daily life and brought new life to design after the wide application of electronic equipment. Especially in the field of public art, its interactive characteristics put forward increasingly higher requirements on how to efficiently extract effective information from the environment and people [1, 2]. As a technology to obtain consistent and comprehensive information, multisensor fusion brings a new method to public art design. Public art can be divided into two parts: public and art, and it can also be understood as a unique art form with public meaning. Among them, public means that works of art can make people have the desire for free communication. Hence, how to let the public freely participate in the creation of art works and form real public art is a problem that researchers need to solve [3].Researchers have also done a lot of research work. Xiao [4] pointed out that multisensor data fusion technology has an important practical application; Dempster/Shafer (D–S) evidence theory is widely used in various fields of information fusion because of its flexibility and effectiveness in modelling and processing uncertain information without considering a priori probability; however, results contrary to intuition may occur in the fusion of highly conflicting evidence. A new multisensor data fusion method based on the divergence measure of evidence and belief entropy was proposed to solve this contradiction. First, a new belief Jensen-Shannon divergence was designed to measure the degree of difference and conflict between evidence; then, the reliability of evidence was expressed by credibility. Numerical examples showed that this method was feasible and effective in dealing with conflict evidence, and the target confidence reached 99.05%. Finally, the effectiveness of the method was verified. The results show that this method is superior to other related methods, and the basic belief value of the real goal is 89.73%. Nada et al. [5] studied the application of multisensor data fusion technology in wheelchair position estimation under the condition of indoor ambient noise measurement. The designed measuring system was based on two odometers placed on the axis of the wheel and combined with a magnetic compass to determine the position and direction. The displacement was measured by an accelerometer. The system combined the data from the sensor as the input of the unscented Kalman filter. Two data fusion architectures were proposed: measurement fusion and state vector fusion. The comparative study of the two shows that the state estimation provided by the measurement fusion architecture has less uncertainty than the state vector fusion. However, after the accelerometer measurement, the position measured by the odometer has relatively high uncertainty. Thereby, fusion is needed in the navigation system. Bouain et al. [6] believed that multisensor architecture provides better environmental perception and understanding for intelligent vehicles; using multiple sensors to handle sensing tasks in a rich environment is a natural solution; most of the research work focuses on the implementation of perceptual tasks based on PC, and few people focus on customized embedded design. A multisensor data fusion embedded design was proposed for vehicle sensing tasks using stereo cameras and light detection and ranging sensors. Gu et al. [7] pointed out that attitude and heading measurement accuracy and system real-time performance are the basic indicators for evaluating attitude and heading reference systems (AHRS). The principle and algorithm of attitude and heading detection based on multisensor fusion were proposed for the heading reference system. Firstly, the information of the system itself was used to judge the motion state of the carrier in the filtering period. Then, accordingly to the motion state, the Kalman filter was carried out by using different measurement information to correct the attitude error angle caused by gyro drift. On this basis, an attitude fusion algorithm based on extended Kalman filter technology was designed for the time update process of the Kalman filter. The results show that the extended Kalman filter algorithm designed according to the simulation results can realize multisensor information fusion, improve the measurement accuracy and realize accurate attitude positioning to provide a simpler and more flexible criterion for the motion state of the carrier. Ozgundogdu et al. [8] held that in the western world, art in the 1960s moved from galleries and museums to public areas; under the definition of public art, it was reflected in multiple artistic practices using various materials, technologies and forms. In the 1970s, public art was regarded as a part of urban design, and artists began to carry out interdisciplinary work with space designers; public works of art appeared in urban design, from landscape design to architectural element design, from placement to urban furnishings or surface design. These works showed different forms in the aspects of modern technology and possibility. In the design process, there were many considerations of public art from concept formation to urban space composition. Public works of art traditionally required works of art to be simply placed in public space. In this context, public art traditionally includes multiple works of art that are produced every day or are being produced in various fields. These works of art can enter through open or closed doors. Most of them are found in the discipline of sculpture, using stone, metal, wood and similar materials, as well as ceramics resistant to outdoor effects. Di-Zi [9] pointed out that there is an inseparable relationship between traditional urban culture and traditional urban context. Compared with the public art in Beijing, Shanghai and Guangzhou, the public art in Hefei has problems of insufficient innovation ability, single expression mode and weak expression effect. To solve the above problems, the gap between traditional culture and public art needs to be repaired, and traditional culture needs to be excavated and inherited. Given the above analysis, some deficiencies still exist in many studies of information fusion technology, which can be divided into three points. First, there is no complete theory. At present, most of the multisensor fusion technologies used are based on the actual scene, and the fusion criteria are established according to the characteristics of the actual scene to form the fusion scheme. There is no theoretical framework and fusion model structure with strong universality. Second, there are few practical application fields and insufficient practical applications. Third, there are no clear guidelines and effect evaluation criteria.This paper proposes the concept of exploration to combine multisensor digital fusion technology with public art design and construct a visualization system based on public art design. The advantage of this exploration is to use the interdisciplinary perspective and cross research method to introduce the two methods of “visibility” and “multisensor digital fusion technology” into the field of public art to analyse the characteristics and methods of urban public art creation. This method is a great breakthrough compared with the previous method.The arrangement of the paper is as follows: Section1 introduces the paper. Section 2 describes all the methods and materials used in paper completely. Section 3 talks about the experiments and also analyse them. Section 4 concludes the paper.


## 2. Materials and Methods

In this section, methods to make the research stronger are discussed utterly. First, the research on multisensor digital fusion technology is explained. Its function is to analyse and apply the different sensors data information in different spaces according to different levels. Second section analyses the multisensor information fusion process. It is beneficial to retain the exact information than other levels. Section2.3 consists of study of multisensor sensor information fusion algorithm and working steps of algorithm. The last section made the design of visibility algorithm in public art design.


### 2.1. Research on Multisensor Digital Fusion Technology

Nowadays, public art becomes increasingly popular. Urban public art is a complete art work set in urban public space, which is full of artistic temperament and humanistic atmosphere [10, 11]. In multiple public art programs, the visibility of public art helps to strengthen the communication among the public, the public and society, and shape a harmonious social atmosphere. For a long time in the past, the public accepted public art passively and had almost no right to change. Now, the public needs to actively participate in or even change it, which requires two-way communication of works of art [12, 13]. Interactive and visible thinking plays a guiding role in digital public art, which can be reflected in two points. First, interactivity makes public art avoid alienation. The reason is that digital public art guided by visual and interactive thinking runs through the whole process of digital public art works, from conception to final presentation. However, the interaction of public art works in the traditional sense is only reflected in the final presentation of works [14, 15]. It is this thinking and creative concept that gives full play to the interaction of digital public art, truly makes the participating audience become one of the “creators” of the works, and greatly shortens the distance between the public and art. Second, the real emotion is conveyed to the audience. Works are produced from life, and finally, go back to life. Figure 1 shows the relationship between art and design. Figure 2 presents a typical visible public art design space:Figure 1
Relationship between art and design.Figure 2
Public art design space.The multisensor information fusion technology is also called multisensor data fusion. Its function is to analyse and apply various sensor data information in different time and spaces according to certain standards, so as to obtain the consistent interpretation and description of the tested object and realize decision-making and estimation. Its advantage is to increase the reliability and credibility of information. Similar to the human brain, multisensor information fusion also adopts an information centralized processing mode to combine and analyse multiple types of information from different sources to obtain the interpretation or description of the tested object. Figure3 is a multisensor data fusion system:Figure 3
Multisensor data fusion system.


### 2.2. Analysis of Multisensor Information Fusion Process

There are five main parts in the process of information fusion: multisensor information acquisition, information preprocessing, feature extraction, fusion calculation, and recognition results (Figure4).Figure 4
The process of information fusion.According to the different information levels, sensor information fusion can be divided into pixel-level fusion, feature-level fusion and decision-level fusion. Pixel-level fusion refers to the fusion of unprocessed sensor data [16, 17]. Its advantage is to retain more original information and provide more information than other levels. Its disadvantages are large sensor information capacity to be processed, long processing time, poor real-time performance, and poor reliability of unprocessed original information. Hence, pixel-level fusion requires high system error correction capability [18, 19]. Figure 5 is its structure diagram:Figure 5
Schematic diagram of pixel-level fusion.Feature-level fusion belongs to the middle level. Figure6 is its structure diagram:Figure 6
Schematic diagram of the feature-layer fusion.The feature-layer fusion is to extract the features of the processed sensor data and complete the statistical compression of information. It can also classify the extracted feature information. Its advantage is to compress a large amount of information to increase the real-time performance. Moreover, the fusion results provide the required feature information for decision analysis to a great extent [20, 21]. Its disadvantage is the slightly low accuracy. Target state fusion is mostly used in the field of multisensor target tracking, and data fusion mainly completes data matching [22, 23].Unlike the other two fusion methods, decision-level fusion is a high-level fusion. Its purpose is to make the global optimal decision according to a certain way for the completed decisions of each information source. Figure7 is a result diagram.Figure 7
Schematic diagram of the decision-level fusion.The advantages of the decision-level fusion are low broadband requirements for information transmission, strong anti-interference ability, low processing cost of the fusion centre and high flexibility.


### 2.3. Multisensor Information Fusion Algorithm

The commonly used multisensor information fusion algorithms are weighted fusion algorithm, Kalman filter algorithm, fusion algorithm based on D-S evidence theory and fusion algorithm based on fuzzy neural network (FNN). The working principle of the weighted fusion algorithm is to assign an uncertain weight to each sensor information and take the weighted average result as the fusion result, but the difficulty of the method is the weight determination. The workflow of the Kalman filter algorithm is to fuse the multisensor information data in real time. It can not only estimate the current state of the system, but also predict the future state of the system [24, 25]. Kalman filter algorithm can give a statistical optimal estimation for multisensor information fusion. The fusion algorithm based on D-S evidence theory is an optimization based on the Kalman filter algorithm. The optimization point is that the D-S evidence theory algorithm can give decision objectives and reliability without a priori probability. The data collected by multisensor have certain uncertainty due to the influence of various factors in real conditions. Generally, a real number greater than 0 and less than 1 is used to represent the authenticity of the information collected by the sensor, which constitutes the fuzzy set of the system [26, 27]. Then, a fusion result with high reliability is obtained through comprehensive reasoning. Figure 8 displays the flow chart of fusion algorithm based on FNN:Figure 8
Flow chart of fusion algorithm based on FNN.


### 2.4. Design of Visibility Algorithm in Public Art Design Based on FNN

Fuzzy theory is the primary theoretical basis of the fuzzy algorithm when input variables change continuously. The fuzzy algorithm can fully adapt to the changing external environment by referring to the preset fuzzy logic relationship. Regardless of the impact of environmental changes on the actual input variables, the relevant fuzzy algorithms can reasonably control the output parameters to obtain more accurate results [28, 29]. Figure 9 shows the composition of the fuzzy reasoning system. There are four main functional modules, namely, fuzzy processing module, fuzzy reasoning mechanism, expert knowledge base and anti-fuzzy processing [30].Figure 9
Schematic diagram of fuzzy reasoning system.There are four steps in the reasoning process of the fuzzy system in Figure9 in executing IF-THEN rules:(1)
The input variables are read in and the membership degree of each output language identity is calculated(2)
The input variables are calculated and the weight of fuzzy rules is calculated(3)
The weight of each fuzzy rule calculated in the previous step is used to obtain the output of the fuzzy rule(4)
The output of each fuzzy rule is superimposed to obtain the total output of the systemFigure9 shows that the fuzzy controller is the basic part of the overall framework of the fuzzy algorithm. Fuzzy controller mainly includes fuzziness, fuzzy reasoning, clarity and knowledge base. Fuzziness is the main process of fuzzy control and its basic goal is to fuzzy the input and convert it into a specific fuzzy quantity. There are two main steps for Fuzziness. First, the input composed of system deviation and changing parameters is fuzzed. Second, the processed fuzzy variables are explored and analysed in the system. The knowledge base is also the main part of the fuzzy controller. The corresponding conversion factors and fuzzy values are input into a specific database. Then, the input data are sorted and modelled according to expert opinions and accumulated experience to form a supporting knowledge base. Fuzzy evaluation is to make the fuzzy decision according to the system’s needs. Different fuzzy rules contain different relationship processing methods, and different decisions also provide matching control rules for various fuzzy rules. Clarity is the operation of converting fuzzy variables into the clear variable.The structure of the dynamic FNN is an extended form based on the Radial Basis Function (RBF), which is functionally equivalent to the basic structure of the fuzzy system. The first layer of the dynamic FNN is the input layer, and each node represents an input language variable. The second layer is the membership function layer, and each node represents a membership function. The third layer is the T-norm layer, and each node represents a part of possible simulation rules, so the number of nodes in this layer reflects the number of fuzzy rules, whereX, C are input variables of equation and equation depend on values of these variables. (1) is the Rj output of rule j:(1)Rj=exp∑i=1rx−cij2σj2=expX−Cjσj2;j=1,2,...,u,where X=x1,x2,...xr,Cj=c1j,...crj is the center of the j-th RBF neural network unit. (3) suggests that each node of this layer represents an RBF unit. The fourth layer is the normalization layer. The nodes in this layer are called n nodes. The number of n nodes is equal to the number of fuzzy rule nodes. The fifth layer is the output layer, and the output of this layer is the superposition of all input signals.(2)yX=∑k=1uωk.φj,where y is the output of variable layer and ωk is the connection weight of rule K.The effective radius and error index of each RBF unit are gradually reduced based on the monotone decreasing function. Network learning at this stage is called rough learning. The parameters indmax, dmin, kd and ke are determined by equations (3)–(6):(3)dmax=Arg maxdij,(4)dmin=Arg mindij,(5)kd=maxdmax∗γi,dmin,(6)ke=maxemax∗βi,emin.In equations (3)–(6), emax is the maximum error preset by the system, emin is the expected accuracy of DFNN, β0<β<1 is the convergence constant, dmax is the maximum length of input space, dmin is the preset minimum length, and γ0<γ<1 is the attenuation constant.New fuzzy rules are constantly produced in the training process. Simulation results show that the width of the RBF unit greatly affects the generalization performance of the network. If the width is less than the distance between adjacent inputs, the generalization ability of FNN will be quite poor, and it will not be able to give correct output to unknown inputs; if the width is too large, the unit is easily saturated and its output will be large no matter how many inputs are. (7) and (8) present the initial parameters of the newly generated rule:(7)Ci=Xi,(8)σi=k∗dmin,where k is the overlap factor. The network has not been established when the first observation data X1,t1 is input, so the data are selected as the first fuzzy rule, that is, C1=X1 and σ1=σ0, where σ0 is the preset parameter. According to the generation basis of the new rule, a new rule needs to be added only when the two conditions ei>ke and ddmin are met at the same time. Otherwise, it shall be discussed according to the following three situations.(1)
When the two conditions are not met, the network can fully accommodate the observation data, and no operation is required on the network.(2)
When the first condition is not satisfied and the second condition is satisfied, it shows that the network meets the system accuracy requirements and has good generalization ability, but the parameters still need to be adjusted.(3)
When the first condition is satisfied and the second condition is not satisfied, it indicates that the generalization ability of the RBF unit covering the input data is not good, and a new RBF unit needs to be generated. TheK-th RBF unit closest to the observation data Xi is adjusted according to the form in (9)σki=kω∗σki−1,wherekω is the present parameter.


## 2.1. Research on Multisensor Digital Fusion Technology

Nowadays, public art becomes increasingly popular. Urban public art is a complete art work set in urban public space, which is full of artistic temperament and humanistic atmosphere [10, 11]. In multiple public art programs, the visibility of public art helps to strengthen the communication among the public, the public and society, and shape a harmonious social atmosphere. For a long time in the past, the public accepted public art passively and had almost no right to change. Now, the public needs to actively participate in or even change it, which requires two-way communication of works of art [12, 13]. Interactive and visible thinking plays a guiding role in digital public art, which can be reflected in two points. First, interactivity makes public art avoid alienation. The reason is that digital public art guided by visual and interactive thinking runs through the whole process of digital public art works, from conception to final presentation. However, the interaction of public art works in the traditional sense is only reflected in the final presentation of works [14, 15]. It is this thinking and creative concept that gives full play to the interaction of digital public art, truly makes the participating audience become one of the “creators” of the works, and greatly shortens the distance between the public and art. Second, the real emotion is conveyed to the audience. Works are produced from life, and finally, go back to life. Figure 1 shows the relationship between art and design. Figure 2 presents a typical visible public art design space:Figure 1
Relationship between art and design.Figure 2
Public art design space.The multisensor information fusion technology is also called multisensor data fusion. Its function is to analyse and apply various sensor data information in different time and spaces according to certain standards, so as to obtain the consistent interpretation and description of the tested object and realize decision-making and estimation. Its advantage is to increase the reliability and credibility of information. Similar to the human brain, multisensor information fusion also adopts an information centralized processing mode to combine and analyse multiple types of information from different sources to obtain the interpretation or description of the tested object. Figure3 is a multisensor data fusion system:Figure 3
Multisensor data fusion system.


## 2.2. Analysis of Multisensor Information Fusion Process

There are five main parts in the process of information fusion: multisensor information acquisition, information preprocessing, feature extraction, fusion calculation, and recognition results (Figure4).Figure 4
The process of information fusion.According to the different information levels, sensor information fusion can be divided into pixel-level fusion, feature-level fusion and decision-level fusion. Pixel-level fusion refers to the fusion of unprocessed sensor data [16, 17]. Its advantage is to retain more original information and provide more information than other levels. Its disadvantages are large sensor information capacity to be processed, long processing time, poor real-time performance, and poor reliability of unprocessed original information. Hence, pixel-level fusion requires high system error correction capability [18, 19]. Figure 5 is its structure diagram:Figure 5
Schematic diagram of pixel-level fusion.Feature-level fusion belongs to the middle level. Figure6 is its structure diagram:Figure 6
Schematic diagram of the feature-layer fusion.The feature-layer fusion is to extract the features of the processed sensor data and complete the statistical compression of information. It can also classify the extracted feature information. Its advantage is to compress a large amount of information to increase the real-time performance. Moreover, the fusion results provide the required feature information for decision analysis to a great extent [20, 21]. Its disadvantage is the slightly low accuracy. Target state fusion is mostly used in the field of multisensor target tracking, and data fusion mainly completes data matching [22, 23].Unlike the other two fusion methods, decision-level fusion is a high-level fusion. Its purpose is to make the global optimal decision according to a certain way for the completed decisions of each information source. Figure7 is a result diagram.Figure 7
Schematic diagram of the decision-level fusion.The advantages of the decision-level fusion are low broadband requirements for information transmission, strong anti-interference ability, low processing cost of the fusion centre and high flexibility.


## 2.3. Multisensor Information Fusion Algorithm

The commonly used multisensor information fusion algorithms are weighted fusion algorithm, Kalman filter algorithm, fusion algorithm based on D-S evidence theory and fusion algorithm based on fuzzy neural network (FNN). The working principle of the weighted fusion algorithm is to assign an uncertain weight to each sensor information and take the weighted average result as the fusion result, but the difficulty of the method is the weight determination. The workflow of the Kalman filter algorithm is to fuse the multisensor information data in real time. It can not only estimate the current state of the system, but also predict the future state of the system [24, 25]. Kalman filter algorithm can give a statistical optimal estimation for multisensor information fusion. The fusion algorithm based on D-S evidence theory is an optimization based on the Kalman filter algorithm. The optimization point is that the D-S evidence theory algorithm can give decision objectives and reliability without a priori probability. The data collected by multisensor have certain uncertainty due to the influence of various factors in real conditions. Generally, a real number greater than 0 and less than 1 is used to represent the authenticity of the information collected by the sensor, which constitutes the fuzzy set of the system [26, 27]. Then, a fusion result with high reliability is obtained through comprehensive reasoning. Figure 8 displays the flow chart of fusion algorithm based on FNN:Figure 8
Flow chart of fusion algorithm based on FNN.


## 2.4. Design of Visibility Algorithm in Public Art Design Based on FNN

Fuzzy theory is the primary theoretical basis of the fuzzy algorithm when input variables change continuously. The fuzzy algorithm can fully adapt to the changing external environment by referring to the preset fuzzy logic relationship. Regardless of the impact of environmental changes on the actual input variables, the relevant fuzzy algorithms can reasonably control the output parameters to obtain more accurate results [28, 29]. Figure 9 shows the composition of the fuzzy reasoning system. There are four main functional modules, namely, fuzzy processing module, fuzzy reasoning mechanism, expert knowledge base and anti-fuzzy processing [30].Figure 9
Schematic diagram of fuzzy reasoning system.There are four steps in the reasoning process of the fuzzy system in Figure9 in executing IF-THEN rules:(1)
The input variables are read in and the membership degree of each output language identity is calculated(2)
The input variables are calculated and the weight of fuzzy rules is calculated(3)
The weight of each fuzzy rule calculated in the previous step is used to obtain the output of the fuzzy rule(4)
The output of each fuzzy rule is superimposed to obtain the total output of the systemFigure9 shows that the fuzzy controller is the basic part of the overall framework of the fuzzy algorithm. Fuzzy controller mainly includes fuzziness, fuzzy reasoning, clarity and knowledge base. Fuzziness is the main process of fuzzy control and its basic goal is to fuzzy the input and convert it into a specific fuzzy quantity. There are two main steps for Fuzziness. First, the input composed of system deviation and changing parameters is fuzzed. Second, the processed fuzzy variables are explored and analysed in the system. The knowledge base is also the main part of the fuzzy controller. The corresponding conversion factors and fuzzy values are input into a specific database. Then, the input data are sorted and modelled according to expert opinions and accumulated experience to form a supporting knowledge base. Fuzzy evaluation is to make the fuzzy decision according to the system’s needs. Different fuzzy rules contain different relationship processing methods, and different decisions also provide matching control rules for various fuzzy rules. Clarity is the operation of converting fuzzy variables into the clear variable.The structure of the dynamic FNN is an extended form based on the Radial Basis Function (RBF), which is functionally equivalent to the basic structure of the fuzzy system. The first layer of the dynamic FNN is the input layer, and each node represents an input language variable. The second layer is the membership function layer, and each node represents a membership function. The third layer is the T-norm layer, and each node represents a part of possible simulation rules, so the number of nodes in this layer reflects the number of fuzzy rules, whereX, C are input variables of equation and equation depend on values of these variables. (1) is the Rj output of rule j:(1)Rj=exp∑i=1rx−cij2σj2=expX−Cjσj2;j=1,2,...,u,where X=x1,x2,...xr,Cj=c1j,...crj is the center of the j-th RBF neural network unit. (3) suggests that each node of this layer represents an RBF unit. The fourth layer is the normalization layer. The nodes in this layer are called n nodes. The number of n nodes is equal to the number of fuzzy rule nodes. The fifth layer is the output layer, and the output of this layer is the superposition of all input signals.(2)yX=∑k=1uωk.φj,where y is the output of variable layer and ωk is the connection weight of rule K.The effective radius and error index of each RBF unit are gradually reduced based on the monotone decreasing function. Network learning at this stage is called rough learning. The parameters indmax, dmin, kd and ke are determined by equations (3)–(6):(3)dmax=Arg maxdij,(4)dmin=Arg mindij,(5)kd=maxdmax∗γi,dmin,(6)ke=maxemax∗βi,emin.In equations (3)–(6), emax is the maximum error preset by the system, emin is the expected accuracy of DFNN, β0<β<1 is the convergence constant, dmax is the maximum length of input space, dmin is the preset minimum length, and γ0<γ<1 is the attenuation constant.New fuzzy rules are constantly produced in the training process. Simulation results show that the width of the RBF unit greatly affects the generalization performance of the network. If the width is less than the distance between adjacent inputs, the generalization ability of FNN will be quite poor, and it will not be able to give correct output to unknown inputs; if the width is too large, the unit is easily saturated and its output will be large no matter how many inputs are. (7) and (8) present the initial parameters of the newly generated rule:(7)Ci=Xi,(8)σi=k∗dmin,where k is the overlap factor. The network has not been established when the first observation data X1,t1 is input, so the data are selected as the first fuzzy rule, that is, C1=X1 and σ1=σ0, where σ0 is the preset parameter. According to the generation basis of the new rule, a new rule needs to be added only when the two conditions ei>ke and ddmin are met at the same time. Otherwise, it shall be discussed according to the following three situations.(1)
When the two conditions are not met, the network can fully accommodate the observation data, and no operation is required on the network.(2)
When the first condition is not satisfied and the second condition is satisfied, it shows that the network meets the system accuracy requirements and has good generalization ability, but the parameters still need to be adjusted.(3)
When the first condition is satisfied and the second condition is not satisfied, it indicates that the generalization ability of the RBF unit covering the input data is not good, and a new RBF unit needs to be generated. TheK-th RBF unit closest to the observation data Xi is adjusted according to the form in (9)σki=kω∗σki−1,wherekω is the present parameter.


## 3. Results and Discussion

### 3.1. Test Results of the Dynamic FNN Model

Figure10(a) shows the root mean squared error transformation during the training of the constructed dynamic FNN model. Figure 10(b) presents the comparison between the model test output and the actual output and the change of test error.Figure 10
Model test results ((a) root mean squared error transformation (b) comparison between model training output and actual output).
(a)(b)The simulation results show that the model network has a compact structure, the root mean squared error of model training is 0.0261, the network has a good fitting effect on the expected output value, and the generalization ability of the network is good.Figure11(a) shows the comparison between the model test output and the actual output. Figure 11(b) shows the model test error.Figure 11
Model validation results ((a) comparison between model test output and actual output (b) test error).
(a)(b)Figure11 reveals that the consistency between the test output value and the actual value is high, the fitting effect is very good, the average absolute error of the test is 0.0147, and the established model achieves the identification accuracy. The experimental results show the superiority of dynamic FNN in nonlinear system identification.


### 3.2. Model Design Analysis

The square design in the city is taken as an example. When people enter the square, it is supposed that each person is given a piece of colour wave point paper, and people use the way of pasting wave point paper to express the display behaviour. If there is no wave point paper, people will have the implicit expression of their visit experiences, such as facial expression, step size, speed and direction. The implicit expression of the public can be captured using a multisensor fusion system. In the sensor fusion system, the camera device and pressure sensor are used to detect the crowd. The data obtained by the two kinds of sensors need to be fused in time and space, in which time fusion refers to the fusion of the data detected at different time points. Spatial fusion refers to the one-time fusion processing of multisensor data. Figure12 is the designed model:Figure 12
Schematic diagram of the designed model.According to the relevant research data, the visibility development of multisensor digital fusion technology in public art design in a complex environment is analysed. The development trend of visibility of public art design in the world and China in the next 14 years is drawn. Figure13 presents the results.Figure 13
Development trend of visibility of public art design.Figure13 shows the visibility development level of public art design in other countries and China in the next 14 years. The visibility level of public art design in China is very low in the initial stage, only 0.25%, then increases rapidly in the next six years, and finally falls into a bottleneck period. The growth rate begins to slow down, but it is still growing. The visibility development level of public art design in other countries in the next 14 years is similar to that in China in the initial stage and then increases rapidly. The final level is slightly higher than that in China.


## 3.1. Test Results of the Dynamic FNN Model

Figure10(a) shows the root mean squared error transformation during the training of the constructed dynamic FNN model. Figure 10(b) presents the comparison between the model test output and the actual output and the change of test error.Figure 10
Model test results ((a) root mean squared error transformation (b) comparison between model training output and actual output).
(a)(b)The simulation results show that the model network has a compact structure, the root mean squared error of model training is 0.0261, the network has a good fitting effect on the expected output value, and the generalization ability of the network is good.Figure11(a) shows the comparison between the model test output and the actual output. Figure 11(b) shows the model test error.Figure 11
Model validation results ((a) comparison between model test output and actual output (b) test error).
(a)(b)Figure11 reveals that the consistency between the test output value and the actual value is high, the fitting effect is very good, the average absolute error of the test is 0.0147, and the established model achieves the identification accuracy. The experimental results show the superiority of dynamic FNN in nonlinear system identification.


## 3.2. Model Design Analysis

The square design in the city is taken as an example. When people enter the square, it is supposed that each person is given a piece of colour wave point paper, and people use the way of pasting wave point paper to express the display behaviour. If there is no wave point paper, people will have the implicit expression of their visit experiences, such as facial expression, step size, speed and direction. The implicit expression of the public can be captured using a multisensor fusion system. In the sensor fusion system, the camera device and pressure sensor are used to detect the crowd. The data obtained by the two kinds of sensors need to be fused in time and space, in which time fusion refers to the fusion of the data detected at different time points. Spatial fusion refers to the one-time fusion processing of multisensor data. Figure12 is the designed model:Figure 12
Schematic diagram of the designed model.According to the relevant research data, the visibility development of multisensor digital fusion technology in public art design in a complex environment is analysed. The development trend of visibility of public art design in the world and China in the next 14 years is drawn. Figure13 presents the results.Figure 13
Development trend of visibility of public art design.Figure13 shows the visibility development level of public art design in other countries and China in the next 14 years. The visibility level of public art design in China is very low in the initial stage, only 0.25%, then increases rapidly in the next six years, and finally falls into a bottleneck period. The growth rate begins to slow down, but it is still growing. The visibility development level of public art design in other countries in the next 14 years is similar to that in China in the initial stage and then increases rapidly. The final level is slightly higher than that in China.


## 4. Conclusions

Interdisciplinary research method and cross research method are used to integrate visibility and multisensor digital fusion technology with public art technology. The impact of multisensor digital fusion on art creation is expounded with the impact of multisensor digital fusion technology on public art as the starting point. Then, advanced science and technology and public art design are combined to study interaction as its important feature. Such work is a good innovation compared with the previous work. Public art is not only analysed from the perspective of art and design, but also from comprehensive perspectives, such as the technical means of computer science, the mode of psychological cognition, and the philosophical thinking of the change and influence of public art on human behavior. However, some deficiencies still exist, which are mainly reflected in the small research area. Only the art works of Asian artists are represented to illustrate the visibility of art space. The research cycle is short, only represented by the works of modern artists. These deficiencies will be improved in the follow-up work.



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*Source: 1295916-2022-04-06.xml*