# The Construction of Virtual Experiment Platform Based on the Fuzzy System

Authors: Fan Yang; Yutai Rao; Ke Wu
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Virtual experiment platform is an open network virtual experiment system, and it is constructed by virtual reality technology. It virtualizes and digitizes experimental resources in the current virtual experiment platform. Virtual experimental platform is similar to real experimental platform. Users can adjust the virtual instrument to complete the experiment process. In this paper, a virtual experiment platform model of the fuzzy system is built. Based on the acquired correlative variables of the fuzzy system, a virtual experiment platform model of the fuzzy system based on the recursive fuzzy network is established. And the model is trained and modified by using the running data of the actual fuzzy algorithm, and the prediction of the fuzzy system in the process of virtual experiment platform is realized. The experimental results show that the virtual experiment platform model of the fuzzy system based on the recursive fuzzy neural network can obtain higher prediction accuracy. Compared with standard fuzzy neural network and other modeling methods, the model of virtual experiment platform of the fuzzy system proposed in this paper can obtain better prediction effect. So, the virtual experiment platform based on the fuzzy algorithm is feasible. The virtual experiment simulation platform of the fuzzy system is built, and the experiment design and research are carried out through the virtual experiment simulation platform.


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## Body

## 1. Introduction

With the development of Internet technology, virtual experiment technology can solve many problems in the virtual experiment platform, for example, the shortage of experimental equipment, insufficient capital investment, lack of experimental equipment due to the increase in the number of college users, and potential safety hazards in the real experimental environment, etc., and the advantages of virtual experimental technology can be said to be obvious. The virtual experiment technology will promote the development of virtual experiment platform in China. Based on the above research background and technology, the virtual experiment simulation experiment platform of the fuzzy system is designed and developed [1, 2]. The design of the simulation experiment platform includes the main interface of the simulation platform, virtual instruments, and experimental rules. According to the specific experimental requirements, the user drags the corresponding virtual instrument in the instrument library to the experimental editing area, connects the virtual instruments together according to the experimental principle and experimental rules, debugs the experiment, and finally runs, views, analyzes the experimental results, and finally reaches the experimental expectation.Ding et al. had shown that virtual experiment teaching has the same effect as traditional teaching and can attract more students’ interest [3]. The virtual experiment platform has entered the real field of scientific research and has an irreplaceable position in some design fields. The original software was gradually upgraded to the online version [4–7]. For example, a series of laboratories have shifted from relying on development to relying on network programming languages. Usability had become a new feature. The virtual experiment platform model is an organizational model of the interaction between the organizer and the participants in teaching and learning. The teaching mode varies from person to person and has strong operability and does not require general use. It should start with stimulating students’ thinking and research skills [8]. The virtual experiment platform system is the basis for the organization and implementation of the virtual experiment platform. It can be defined as a network computer teaching system that simulates the real experiment of virtual reality technology. Niu and Li proposed the fuzzy set theory, which refers to the theory of using the basic concepts of fuzzy sets or continuous membership functions [9]. It can be divided into five branches: fuzzy mathematics, fuzzy logic and artificial intelligence, fuzzy systems, uncertainty and information, and fuzzy decision-making. Most applications of fuzzy theory focus on fuzzy systems. In recent years, the general approximation theory of fuzzy systems has been greatly developed, and various fuzzy systems have been proved to be general function approximators [10–14]. A large number of researches and practices have proved that fuzzy systems have great advantages in dealing with complex nonlinear problems. Therefore, the modeling of fuzzy systems is an effective method to solve complex problems. The identification of fuzzy systems is an important means of fuzzy system modeling and has become a research hotspot in the field of modern control.Adaptive fuzzy control is inherently different than traditional adaptive control. Adaptive fuzzy control can make the control system adapt to the uncertainty of the controlled object by using the language fuzzy information and adaptive function provided by the expert, and it can work by identifying the parameters of the system [15]. The traditional adaptive controller has automatic control functions. This is very important for systems with high uncertainty, such as chemical reaction processes or aircraft. Although these systems are difficult to control from a control theory perspective, operators can successfully control such systems [16]. Therefore, adaptive fuzzy control provides a tool for people to use fuzzy information. At present, the more mature adaptive fuzzy control type is a self-correcting fuzzy controller with quantization factor and scale factor. In adaptive fuzzy control, the most effective real-time control methods are quantization factors and scaling factors [17–19]. The online identification and self-tuning fuzzy control of the quantization factor and the scale factor are based on the control effect of the controller [20, 21].For the accuracy requirements of control systems, linear system theory is often used to deal with most of the current engineering and technical problems and can draw reasonable conclusions within a certain range [22]. Therefore, nonlinear conditions in actual systems are often ignored or replaced with various relationships. This is the main reason why nonlinear system theory has not been concerned and developed for a long time. The nonlinearity of a fuzzy system is usually a simplification or approximation of nonlinearity or a special case of nonlinearity. In this paper, the virtual experiment platform model of fuzzy system is built [23]. Based on the obtained fuzzy system correlation variables, a virtual experiment platform model of the fuzzy system based on recursive fuzzy network is established, and the model is trained and revised by using the operational data of the actual fuzzy algorithm [24–26]. The prediction of the fuzzy system in the virtual experiment platform process is realized. The research results show that the proposed virtual experiment platform is feasible by using the fuzzy algorithm as an example. By constructing the virtual experiment simulation platform of the fuzzy system, the experimental design and research are carried out through the virtual experiment simulation platform [27, 28].


## 2. Proposed Method for Constructing a Virtual Experimental Platform

### 2.1. Fuzzification

The process of fuzzification is to convert the exact value of the input variable into a fuzzy variable. It is the input interface to the fuzzy controller. The relevant physical quantity of the controlled object is converted to electricity by the sensor. If the output of the sensor is a continuous analog quantity, the digital quantity is converted to an input measurement by the A/D converter and then normalized. Fuzzy reasoning is different, and the corresponding language scores are different. The objective value and the actual value constitute a deviation. The fuzzy control controls the system based on the deviation. In the case of dual input, the rate of change of deviation should also be considered. These two inputs are exact values. The combined operation is actually the operation of the membership function. The fuzzy variable is converted to the interval [0, 1], 0, and 1 which corresponds to the membership of the clear set element. The most commonly used membership functions are triangles, Gaussians, trapezoids, and bells. Different membership functions are selected according to different situations. The triangle membership function is expression (1):
(1)uAjx=0,x<a,x−ab−a,a≤x<b,x−cb−c,b≤x≤c,0,x>c.The Gaussian membership function is expression (2):
(2)uAix=ex-ai2/bi2.The trapezoidal membership function is expression (3):
(3)uAjx=x−ab−a,a≤x<b,1,b≤x<c,0,x<a,d−xd−c,c<x≤d,0,x>d.The bell-shaped membership function is expression (4):
(4)uAi=11+x−ai2.The bell-type membership function is not commonly used, because the calculation is difficult, and the system uses a triangular membership function. In this field, it is divided into different levels, with many fine-grained rules, but the programming is complicated. There are simple rules, but the rough control effect is difficult to achieve the desired results. In the control system, considering the simplicity and the control effect, it is divided into seven levels, in which the positive and negative directions are indicated. Artificial neural network (ANN) is a simplified model that is built using mathematical and physical methods and modern computer techniques to abstract the basic structure and function of the human brain neural network. By simulating the structure and activity mechanism of the human brain nervous system, the artificial neural network presents many features of the human brain and has some basic functions of the human brain. The relationship between the input and output of the system can be reflected by fuzzy control rules. Fuzzy reasoning must be based on fuzzy rules, which is the key to the entire fuzzy control algorithm. The establishment of fuzzy control rules requires a large amount of expert experience and knowledge to carry out a large number of experiments and accumulate long-term learning. Artificial neural networks mainly include the interconnection of basic units, the distribution of information storage, and the parallelism of information processing, network nonlinearity, fault tolerance and computational ambiguity, and network adaptability and other properties. The basic functions of ANN include associative memory, nonlinear mapping, and classification and recognition. The ANN model is the most vivid, most appropriate, and intuitive model of the brain structure and working principle known to man and the most direct means to study artificial intelligence. In recent years, the ANN has developed rapidly with the unremitting pursuit and unremitting efforts of researchers, and its application fields are also expanding. The module is designed to expand the clear value. Their role is to make the variables scale up or down in order to fit well with adjacent modules. The function of the quantization factor is to transform the physical domain of the input signal into a fuzzy control fuzzy domain, and the scaling factor is to convert the clarified fuzzy domain into a physical domain to control the controlled object.


### 2.2. Metamodel

The obfuscation method proposed in this paper is also reflected in the hypermodel. The metamodel is the model of the model. It emphasizes the description of modeling languages at a higher level of abstraction than the model language itself [29]. It is a description of information such as model building, semantic description, and integration operations. It can provide a better solution for data integration. The first metamodel (CIDF) is defined by the Electronic Industries Association (EIA), and the metamodel (MOF) is defined by the Object Management Organization (OMG). These metamodels are based on the classic four-layer metadata architecture. The concept of a metamodel can be described in terms of data-based, process-based, and executable aspects. Its main core is to improve objects and find common features. As its basic model, it can embody the corresponding objects based on these basic models. The metamodel is designed to meet integrity, transformation, and self-adjustability [30]. In the design process, we mainly consider the basic characteristics of the description object, attributes, and generation rules, including structure, behavior, and other information to guide the construction of the corresponding object. Metamodel-based modeling, or metamodeling, is a higher-level abstraction of activities and systems that create metamodels. Metamodeling is a model of integrated engineering analysis that allows a set of multiple models to be created in a particular domain, the metamodel in that domain. The commonly accepted metamodel conceptual framework is based on four-tier architecture metamodels, metamodels, models, and instances. The metamodel is mainly used in three aspects: as a summary of semantic data to be exchanged or stored, as a language that supports a particular method or process, and as a language that expresses additional semantics of existing information. In this paper, the metamodel is based primarily on experimental components. It simulates the properties, operations, and business processes of an experimental component, provides a uniform interface and mode of operation, defines a uniform specification, and then assembles and queries the application.The metamodel is the abstraction of things. It focuses on the properties of the thing itself, follows specific rules, and is the same as the ontology as a set of semantics in a particular domain, but the metamodel must be an ontology, and not every ontology can be defined as a metamodel. To build a metamodel, we need to start with the elements of the metamodel, abstract things, and then instantiate them. The object theme is to describe the composition and relationship of the modeled object. Constructive composition is a related concept and element that explains things from a static perspective. Take the modeling language as an example, it can correspond to the classes, properties, and operations associated with them. Interrelationships are dynamic descriptions of interactions and state transitions between elements. Similarly, in “modeling languages,” the relationships between them are primarily related, implemented, generalized, extended, and dependent. A rule is a set of semantics. The description of the subject matter requires uniform requirements for readability, completeness, and consistency. Rule settings are a set of rules with corresponding specifications in terms of naming, scope of operation, visibility, and so on. The description mechanism is a strict specification and corresponding textual description of various entities and relationships in the model construction process, providing a unified visual representation. The description mechanism should give a generic partition of the system that follows some modeling principles. On the basis of clarifying the elements of metamodel construction, the metamodel can be explained from two aspects: construction and behavior.


### 2.3. Virtual Experiment Platform

The virtual experiment teaching system is the platform foundation for the organization and implementation of virtual experiment teaching. It can be defined as a network computer teaching system that simulates real-world experiments with virtual reality technology. It can generally be divided into two parts: virtual experiment platform and virtual experiment teaching management. The virtual experiment platform can be regarded as a virtual laboratory, which can be divided into virtual experiments based on network, virtual experiments of a certain subject in a specific space, technical demonstration, device simulation, and system simulation [31]. Western developed countries have been actively studying virtual laboratories. In the early days, Western countries had strong scientific research capabilities and invested enough money in virtual laboratory research. The purpose of experimental teaching is to deepen students’ understanding of theoretical knowledge, cultivate their ability to observe and analyze problems, master basic experimental research methods and skills, and stimulate students’ interest in learning. Experimental teaching is a two-way interactive activity between organizers, teachers, and participants. The organizational mode and interactive mode of teaching content must follow certain rules to enhance students’ desire to learn. It involves two aspects: the learning psychology of the learner and the organization of the learning object. The learner’s learning psychology, that is, from the learner’s subject, through the study of the body’s learning process, forms an appropriate experimental teaching model and design. The main learning theory is behaviorism theory, which was formed in the early 20th century. Typical theories include Pavlov’s classical conditioning theory, Skinner’s operational conditioning theory, and Sandwich’s behavioral theory. They can generally be divided into classic behavioral learning theory and operational learning theory. On the one hand, it believes that learning is a relatively long-lasting change in behavior caused by repeated empirical practice. The four basic elements of behavioral learning theory are internal driving, clues, reactions, and reinforcement. The internal dynamics evolve into learning motivation. The response is a positive response to stimuli, and reinforcement is a feedback that improves learning.At present, the research on virtual experiments in western developed countries has achieved many scientific results. In the early stages, the main purpose of foreign virtual laboratory research was to use experimental teaching, research, education and training, and many research institutions and enterprises participated in the research. Many research projects in virtual laboratories can be supported by a large amount of state funds; so, the research of foreign virtual laboratories is very rapid. The popularity of virtual laboratories in many universities is also very high, which further accelerates the development of virtual laboratories abroad. Many research institutes or universities abroad have developed virtual laboratories in related fields and applied them to this field. PhET is an interactive virtual simulation software developed by the University of Colorado. Students can use PhET for inquiry learning. If students are interested in something in the physical world, it can help students with research and exploration. This virtual simulation software has good interactivity. Users can use this virtual simulation software to complete research in highly simulated virtual environments. User research can achieve twice the result with half the effort. The Livechem Virtual Chemistry Laboratory was designed and developed by the University of Oxford, UK, using Flash technology to perform experimental operations and demonstrations. Users can select the chemicals required for the experiment in the virtual lab and then complete the different experiments according to the specific experimental requirements. ModelehemLab is a chemical software developed and designed by McMaster University, Canada, with a variety of chemical laboratory instruments. The user can select the experimental instrument according to his own needs, can assemble the selected experimental instrument arbitrarily, and then complete the experimental operation according to the prompts in the experiment process. In some experimental operations, users can skillfully learn the experimental principles quickly.


## 2.1. Fuzzification

The process of fuzzification is to convert the exact value of the input variable into a fuzzy variable. It is the input interface to the fuzzy controller. The relevant physical quantity of the controlled object is converted to electricity by the sensor. If the output of the sensor is a continuous analog quantity, the digital quantity is converted to an input measurement by the A/D converter and then normalized. Fuzzy reasoning is different, and the corresponding language scores are different. The objective value and the actual value constitute a deviation. The fuzzy control controls the system based on the deviation. In the case of dual input, the rate of change of deviation should also be considered. These two inputs are exact values. The combined operation is actually the operation of the membership function. The fuzzy variable is converted to the interval [0, 1], 0, and 1 which corresponds to the membership of the clear set element. The most commonly used membership functions are triangles, Gaussians, trapezoids, and bells. Different membership functions are selected according to different situations. The triangle membership function is expression (1):
(1)uAjx=0,x<a,x−ab−a,a≤x<b,x−cb−c,b≤x≤c,0,x>c.The Gaussian membership function is expression (2):
(2)uAix=ex-ai2/bi2.The trapezoidal membership function is expression (3):
(3)uAjx=x−ab−a,a≤x<b,1,b≤x<c,0,x<a,d−xd−c,c<x≤d,0,x>d.The bell-shaped membership function is expression (4):
(4)uAi=11+x−ai2.The bell-type membership function is not commonly used, because the calculation is difficult, and the system uses a triangular membership function. In this field, it is divided into different levels, with many fine-grained rules, but the programming is complicated. There are simple rules, but the rough control effect is difficult to achieve the desired results. In the control system, considering the simplicity and the control effect, it is divided into seven levels, in which the positive and negative directions are indicated. Artificial neural network (ANN) is a simplified model that is built using mathematical and physical methods and modern computer techniques to abstract the basic structure and function of the human brain neural network. By simulating the structure and activity mechanism of the human brain nervous system, the artificial neural network presents many features of the human brain and has some basic functions of the human brain. The relationship between the input and output of the system can be reflected by fuzzy control rules. Fuzzy reasoning must be based on fuzzy rules, which is the key to the entire fuzzy control algorithm. The establishment of fuzzy control rules requires a large amount of expert experience and knowledge to carry out a large number of experiments and accumulate long-term learning. Artificial neural networks mainly include the interconnection of basic units, the distribution of information storage, and the parallelism of information processing, network nonlinearity, fault tolerance and computational ambiguity, and network adaptability and other properties. The basic functions of ANN include associative memory, nonlinear mapping, and classification and recognition. The ANN model is the most vivid, most appropriate, and intuitive model of the brain structure and working principle known to man and the most direct means to study artificial intelligence. In recent years, the ANN has developed rapidly with the unremitting pursuit and unremitting efforts of researchers, and its application fields are also expanding. The module is designed to expand the clear value. Their role is to make the variables scale up or down in order to fit well with adjacent modules. The function of the quantization factor is to transform the physical domain of the input signal into a fuzzy control fuzzy domain, and the scaling factor is to convert the clarified fuzzy domain into a physical domain to control the controlled object.


## 2.2. Metamodel

The obfuscation method proposed in this paper is also reflected in the hypermodel. The metamodel is the model of the model. It emphasizes the description of modeling languages at a higher level of abstraction than the model language itself [29]. It is a description of information such as model building, semantic description, and integration operations. It can provide a better solution for data integration. The first metamodel (CIDF) is defined by the Electronic Industries Association (EIA), and the metamodel (MOF) is defined by the Object Management Organization (OMG). These metamodels are based on the classic four-layer metadata architecture. The concept of a metamodel can be described in terms of data-based, process-based, and executable aspects. Its main core is to improve objects and find common features. As its basic model, it can embody the corresponding objects based on these basic models. The metamodel is designed to meet integrity, transformation, and self-adjustability [30]. In the design process, we mainly consider the basic characteristics of the description object, attributes, and generation rules, including structure, behavior, and other information to guide the construction of the corresponding object. Metamodel-based modeling, or metamodeling, is a higher-level abstraction of activities and systems that create metamodels. Metamodeling is a model of integrated engineering analysis that allows a set of multiple models to be created in a particular domain, the metamodel in that domain. The commonly accepted metamodel conceptual framework is based on four-tier architecture metamodels, metamodels, models, and instances. The metamodel is mainly used in three aspects: as a summary of semantic data to be exchanged or stored, as a language that supports a particular method or process, and as a language that expresses additional semantics of existing information. In this paper, the metamodel is based primarily on experimental components. It simulates the properties, operations, and business processes of an experimental component, provides a uniform interface and mode of operation, defines a uniform specification, and then assembles and queries the application.The metamodel is the abstraction of things. It focuses on the properties of the thing itself, follows specific rules, and is the same as the ontology as a set of semantics in a particular domain, but the metamodel must be an ontology, and not every ontology can be defined as a metamodel. To build a metamodel, we need to start with the elements of the metamodel, abstract things, and then instantiate them. The object theme is to describe the composition and relationship of the modeled object. Constructive composition is a related concept and element that explains things from a static perspective. Take the modeling language as an example, it can correspond to the classes, properties, and operations associated with them. Interrelationships are dynamic descriptions of interactions and state transitions between elements. Similarly, in “modeling languages,” the relationships between them are primarily related, implemented, generalized, extended, and dependent. A rule is a set of semantics. The description of the subject matter requires uniform requirements for readability, completeness, and consistency. Rule settings are a set of rules with corresponding specifications in terms of naming, scope of operation, visibility, and so on. The description mechanism is a strict specification and corresponding textual description of various entities and relationships in the model construction process, providing a unified visual representation. The description mechanism should give a generic partition of the system that follows some modeling principles. On the basis of clarifying the elements of metamodel construction, the metamodel can be explained from two aspects: construction and behavior.


## 2.3. Virtual Experiment Platform

The virtual experiment teaching system is the platform foundation for the organization and implementation of virtual experiment teaching. It can be defined as a network computer teaching system that simulates real-world experiments with virtual reality technology. It can generally be divided into two parts: virtual experiment platform and virtual experiment teaching management. The virtual experiment platform can be regarded as a virtual laboratory, which can be divided into virtual experiments based on network, virtual experiments of a certain subject in a specific space, technical demonstration, device simulation, and system simulation [31]. Western developed countries have been actively studying virtual laboratories. In the early days, Western countries had strong scientific research capabilities and invested enough money in virtual laboratory research. The purpose of experimental teaching is to deepen students’ understanding of theoretical knowledge, cultivate their ability to observe and analyze problems, master basic experimental research methods and skills, and stimulate students’ interest in learning. Experimental teaching is a two-way interactive activity between organizers, teachers, and participants. The organizational mode and interactive mode of teaching content must follow certain rules to enhance students’ desire to learn. It involves two aspects: the learning psychology of the learner and the organization of the learning object. The learner’s learning psychology, that is, from the learner’s subject, through the study of the body’s learning process, forms an appropriate experimental teaching model and design. The main learning theory is behaviorism theory, which was formed in the early 20th century. Typical theories include Pavlov’s classical conditioning theory, Skinner’s operational conditioning theory, and Sandwich’s behavioral theory. They can generally be divided into classic behavioral learning theory and operational learning theory. On the one hand, it believes that learning is a relatively long-lasting change in behavior caused by repeated empirical practice. The four basic elements of behavioral learning theory are internal driving, clues, reactions, and reinforcement. The internal dynamics evolve into learning motivation. The response is a positive response to stimuli, and reinforcement is a feedback that improves learning.At present, the research on virtual experiments in western developed countries has achieved many scientific results. In the early stages, the main purpose of foreign virtual laboratory research was to use experimental teaching, research, education and training, and many research institutions and enterprises participated in the research. Many research projects in virtual laboratories can be supported by a large amount of state funds; so, the research of foreign virtual laboratories is very rapid. The popularity of virtual laboratories in many universities is also very high, which further accelerates the development of virtual laboratories abroad. Many research institutes or universities abroad have developed virtual laboratories in related fields and applied them to this field. PhET is an interactive virtual simulation software developed by the University of Colorado. Students can use PhET for inquiry learning. If students are interested in something in the physical world, it can help students with research and exploration. This virtual simulation software has good interactivity. Users can use this virtual simulation software to complete research in highly simulated virtual environments. User research can achieve twice the result with half the effort. The Livechem Virtual Chemistry Laboratory was designed and developed by the University of Oxford, UK, using Flash technology to perform experimental operations and demonstrations. Users can select the chemicals required for the experiment in the virtual lab and then complete the different experiments according to the specific experimental requirements. ModelehemLab is a chemical software developed and designed by McMaster University, Canada, with a variety of chemical laboratory instruments. The user can select the experimental instrument according to his own needs, can assemble the selected experimental instrument arbitrarily, and then complete the experimental operation according to the prompts in the experiment process. In some experimental operations, users can skillfully learn the experimental principles quickly.


## 3. Experimental Platform Construction Experiment

### 3.1. Experimental Component Resource Management

Component resource management is the raw data that organizes and manages various experimental elements. It classifies, retrieves, manages, and organizes the experimental elements themselves and their related operations, algorithms, knowledge points, etc. according to their needs and combines the experiments themselves with the curriculum and knowledge points to achieve an effective connection between the experiment and the curriculum. In this paper, the construction of the experimental element model is based on the different contents of experimental elements, functional classification, knowledge point correlation, topic, and other resource management of organizational experimental elements. The following concepts and attributes are defined in the experimental component resource management. Component unit vomiting refers to individual virtual experimental components, including component structure and operation, which cannot be separated. It is the basic component of virtual experimental teaching resources. The component knowledge unit refers to the algorithms and knowledge points associated with the component unit and is used in conjunction with the component unit. A combination of elements has similar attributes according to certain rules. A component family can consist of a single element or a smaller element. According to different attribute settings, it can be divided into course element family, functional element family, experimental subject element family, and so on. Different component families can be included in each other. The component knowledge series refers one by one to all combinations of algorithms and knowledge points associated with the component family. The experimental platform sets up the resource management model structure as shown in Figure1.Figure 1
Experiment platform builds resource management model structure.


### 3.2. System Design

At present, there are many scholars that developed based on the idea of data-driven research, in addition to a variety of virtual experimental platform model calibration methods, and even some scholars have developed a local linear regression method based on the noncalibrated virtual experimental platform model. The application of the virtual experimental platform method in complex systems such as virtual experiment processes, due to the strong interference and large time-varying characteristics of the virtual experimental process, is necessary to periodically calibrate the virtual experimental platform model to adapt to the changes. Online calibration of the virtual experimental platform must pay attention to the data matching of different types of parameters in the timing. For parameters that are continuously acquired by the online instrument, calibration requires only the same time delay. However, the parameters obtained by offline manual testing have strong randomness and time accuracy differences. Therefore, when calibrating the virtual experimental platform model, special attention should be paid to the time series matching problem. Otherwise, not only can the calibration not be completed but it can also lead to model accuracy of the virtual experimental platform: refused or completely failed.The experimenter can personally operate the experimental process. Users can select virtual instruments, connect virtual instruments, and set experimental parameters. Users and systems are interactive. Therefore, this paper mainly uses the experimental experiment to realize the interaction in the virtual experiment. System flow design is as follows: (1) the user logs into the virtual experiment website and clicks on the virtual experiment simulation platform. (2) On the simulation platform, the user can create a new experiment or open the previous experiment and modify it. (3) When creating a new experiment, the user drags the instrument to the instrument library on the platform with the mouse and places them in the appropriate position in the editing area. Users can set the properties of the instrument and drag and drop them to change the position of the instrument at will. (4) After the experiment is completed, the user can judge whether all instrument ports are connected by clicking the debug button. (5) After the experiment is successfully connected, you can run the experiment through the control button in the toolbar to view the dynamic running process of the experiment. The settings and display of the corresponding experimental parameters can be modified and viewed on the right side of the platform. Once the user completes the experiment, he can save the experiment and submit the experiment so that the user can continue to view the experiment and modify the experiment the next time. The system flow is shown in Figure2.Figure 2
Experimental operating system flow chart.


## 3.1. Experimental Component Resource Management

Component resource management is the raw data that organizes and manages various experimental elements. It classifies, retrieves, manages, and organizes the experimental elements themselves and their related operations, algorithms, knowledge points, etc. according to their needs and combines the experiments themselves with the curriculum and knowledge points to achieve an effective connection between the experiment and the curriculum. In this paper, the construction of the experimental element model is based on the different contents of experimental elements, functional classification, knowledge point correlation, topic, and other resource management of organizational experimental elements. The following concepts and attributes are defined in the experimental component resource management. Component unit vomiting refers to individual virtual experimental components, including component structure and operation, which cannot be separated. It is the basic component of virtual experimental teaching resources. The component knowledge unit refers to the algorithms and knowledge points associated with the component unit and is used in conjunction with the component unit. A combination of elements has similar attributes according to certain rules. A component family can consist of a single element or a smaller element. According to different attribute settings, it can be divided into course element family, functional element family, experimental subject element family, and so on. Different component families can be included in each other. The component knowledge series refers one by one to all combinations of algorithms and knowledge points associated with the component family. The experimental platform sets up the resource management model structure as shown in Figure1.Figure 1
Experiment platform builds resource management model structure.


## 3.2. System Design

At present, there are many scholars that developed based on the idea of data-driven research, in addition to a variety of virtual experimental platform model calibration methods, and even some scholars have developed a local linear regression method based on the noncalibrated virtual experimental platform model. The application of the virtual experimental platform method in complex systems such as virtual experiment processes, due to the strong interference and large time-varying characteristics of the virtual experimental process, is necessary to periodically calibrate the virtual experimental platform model to adapt to the changes. Online calibration of the virtual experimental platform must pay attention to the data matching of different types of parameters in the timing. For parameters that are continuously acquired by the online instrument, calibration requires only the same time delay. However, the parameters obtained by offline manual testing have strong randomness and time accuracy differences. Therefore, when calibrating the virtual experimental platform model, special attention should be paid to the time series matching problem. Otherwise, not only can the calibration not be completed but it can also lead to model accuracy of the virtual experimental platform: refused or completely failed.The experimenter can personally operate the experimental process. Users can select virtual instruments, connect virtual instruments, and set experimental parameters. Users and systems are interactive. Therefore, this paper mainly uses the experimental experiment to realize the interaction in the virtual experiment. System flow design is as follows: (1) the user logs into the virtual experiment website and clicks on the virtual experiment simulation platform. (2) On the simulation platform, the user can create a new experiment or open the previous experiment and modify it. (3) When creating a new experiment, the user drags the instrument to the instrument library on the platform with the mouse and places them in the appropriate position in the editing area. Users can set the properties of the instrument and drag and drop them to change the position of the instrument at will. (4) After the experiment is completed, the user can judge whether all instrument ports are connected by clicking the debug button. (5) After the experiment is successfully connected, you can run the experiment through the control button in the toolbar to view the dynamic running process of the experiment. The settings and display of the corresponding experimental parameters can be modified and viewed on the right side of the platform. Once the user completes the experiment, he can save the experiment and submit the experiment so that the user can continue to view the experiment and modify the experiment the next time. The system flow is shown in Figure2.Figure 2
Experimental operating system flow chart.


## 4. Results and Discussion

After implementing the simulation platform, it is necessary to test the function of the simulation platform. Several representative experimental data in the experiment were selected for testing. Test content includes virtual instrument selection, movement and deletion, virtual instrument connection, lab demonstration, experimental parameter setup, and demonstration. In this paper, the fuzzy algorithm is used to verify the training and prediction of fuzzy soft-measurement models. The main idea of fuzzy algorithm verification is to divide the original data into the training group and verification group. The experiment using the fuzzy algorithm verification method can effectively avoid the overfitting phenomenon of network model training. The training data set is used as network training data, and the verification data set is used to verify whether the training data set has been fitted. The number of stop steps is determined by the curve that validates the error and stops training when the curve reaches a minimum. In order to verify the role of the virtual platform correlation variable screening, based on the aforementioned soft measurement model structure, a variety of data is used to construct the soft measurement model. First, all unprimed principal variables are selected as inputs to the fuzzy neural network. The actual operational data used in the experimental data was selected as 200 training data and 80 as test data. The test results and test errors of the fuzzy neural network model are shown in Figure2.Secondly, the principal variables selected by the above method are selected, and the experimental related data is used as the input of the fuzzy neural network. The fuzzy neural network model using five screened variables as input variables has better test results and test errors than the network test results without screening. The specific effects are shown in Figure3.Figure 3
Virtual platform model test results and errors.The principal elements before and after the dimensionality reduction are, respectively, input as a plurality of data, and the output data is predicted. The comparison of the prediction performance is as shown in Figure4. By comparison, the dimensionality-reduced variable matrix can be used as the model input to obtain better prediction accuracy and shorter prediction time, which can better meet the real-time and reliability requirements of output data detection.Figure 4
Comparison of virtual platform prediction performance for different principal components.By analyzing the characteristics of the graph, it can be seen that it has a clear gradient distribution, indicating that the fuzzy system designed from the error and error changes to the fuzzy map ofU and the theoretical design match well. The center of the gravity method is used to solve the fuzzification problem. For each possible value of E and EC, the corresponding output U is calculated. After calculating the fuzzy relation R based on fuzzy inference, it is calculated using an inference synthesis rule. The fuzzy rule lookup table is shown in Table 1.Table 1
Fuzzy rule query.
ECUE-4-3-2-101234-154330-2-2-2-10532200-1-1-2163210-1-2-1-1In the fuzzy rule observer, the reasoning process of the fuzzy inference system can be graphically depicted in Figure5. The output of the fuzzy logic inference system can be observed by changing the input value of the system. The output feedback controller is constructed by an adaptive fuzzy method based on the estimated state. The three principal element variable data selected are selected as inputs to the recursive fuzzy neural network. Using the same virtual experimental platform as the selection data, the test results and test errors of the output data are shown in Figure 6.Figure 5
Test results of virtual experiment model after dimensionality reduction.Figure 6
Prediction effect of the fuzzy neural model on virtual experiment platform.When the virtual experiment platform system parameter increases by 10%, the output curve of the system’s control law shows a greater oscillation, indicating that the virtual experimental platform system is designed to be a virtual experimental platform system when the system parameters are increased by 10%. Quickly restored to a stable state, the virtual experimental platform system not only has a strong tracking position input signal but also significantly weakens the chattering and improves the stability of the virtual experimental platform system.Based on the above research background and technology, design a virtual experiment simulation experiment platform. The design of the simulation experiment platform includes the main interface of the simulation platform, virtual instruments, and experimental rules. According to the specific experimental requirements, the user drags the corresponding virtual instrument in the instrument library to the experimental editing area, connects the virtual instruments together according to the experimental principle and experimental rules, debugs the experiment, and finally runs, views, analyzes the experimental results, and finally reaches the experimental expectation.Based on the nonlinear mathematical model of the established virtual experimental platform system, the neural fuzzy control strategy is designed to control the virtual experimental platform. The simulation experiment shows that the designed control system can better track the position input signal and blur. Compared with the structure control system, the response speed is fast, and the output response curve is small. The experimental results show that the virtual experimental platform system not only has strong tracking position input signal but also significantly weakens the chattering, which improves the stability of the virtual experimental platform system.


## 5. Conclusions

With the development of Internet technology, virtual experiment technology can solve many problems in experimental teaching. For example, the shortage of experimental equipment and insufficient capital investment, due to the lack of experimental equipment caused by the increase in the number of college students, there may be security risks in the real experimental environment, etc. The advantages of virtual experimental technology can be said to be obvious. The virtual experiment technology will promote the development of experimental teaching in China. The virtual experimental platform model of fuzzy system proposed in this paper can obtain better prediction results. This paper mainly draws the following conclusions.The output of the fuzzy logic inference system can be observed by changing the input value of the system. In this paper, a robust uncertain system observer design is proposed for the nonlinear strict feedback system with unknown control coefficients on the virtual experimental platform. The output feedback controller is then constructed by an adaptive fuzzy method based on the estimated state. It is carried out assuming that the control coefficient of the virtual experimental platform is constant. This result helps to further investigate the case where the virtual control coefficient is a function.



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*Source: 6309633-2022-05-12.xml*