# Evaluation and Analysis of the Intervention Effect of Systematic Parent Training Based on Computational Intelligence on Child Autism

Authors: Xuejin He; Yinzhen Yu; Yanqiong Ouyang
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

Autism, also known as pervasive developmental disorder or autism spectrum disorder, is a group of clinical syndromes of developmental delay or impairment. Social impairment, verbal communication impairment, and behavioral impairment are the three conditions for the diagnosis of autism spectrum disorder, according to the American Psychiatric Association’s Diagnostic and Statistical Manual. According to relevant statistics, about 1 in 100 children is now diagnosed with autism, and their rehabilitation treatment is also valued by people from all walks of life. In the rehabilitation training of autistic children, it is found that the rehabilitation training of autistic children should pay attention to the role of parents and family environment. It is crucial that parents receive systematic training and act as partners in the development of the intervention plan. Research shows that a specific structured education and skills training program for parents of children with autism can be beneficial in improving behavioral problems, functional communication, and symptoms of autism in children with autism. To this end, this paper has completed the following work: Secondly, a portion of the systematic training of CA parents is discussed, followed by an explanation of the structure and principles of BPNN. Finally, the BPNN is utilized to create a model for assessing the impact of systematic parent instruction on CA. The experimental findings suggest that the proposed BPNN outperforms the competition.


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## Body

## 1. Introduction

CA is the most common developmental disorder among the extensive developmental disorders. Its main clinical manifestations are social interaction disorder, language communication disorder, and behavior disorder; often, the onset is before age 3, more boys than girls [1]. CA is a chronic functional disability that develops in childhood, and its long-term prognosis is related to ongoing community and family care [2]. Data released by the US Centers for Disease Control and Prevention in 2014 showed that the prevalence of autism spectrum disorder (ASD) was 1 in 68, with a male-to-female ratio of about 4.5 : 1. Among the mentally disabled children in my country, CA ranks first. Due to the rising prevalence of autism, people’s lives may be plagued by intense and ubiquitous lifelong illnesses, and families are greatly affected emotionally and financially. The etiology and pathological mechanism of CA are still unclear and may involve many factors, such as genetic factors, neurobiological factors, organic brain factors, maternal diseases, infections, and immune factors, but the exact cause and mechanism of the disease have not yet been determined [3]. Children with autism have poor verbal communication, social interaction, and self-care ability; stereotyped behaviors; and narrow interests, and most of them cannot integrate into the normal population. Mental retardation, attention deficit hyperactivity disorder, learning difficulties, sleep issues, eating disorders, seizures, sensory abnormalities, and other comorbidities of children with autism place a severe strain on the family and society. Scholars in the United States and worldwide believe that there is presently no viable treatment to cure autism, and children with autism have a poor prognosis [4]. If children with autism can be detected early, diagnosed early, and carried out timely intervention and other effective rehabilitation education and training, about 5% of children with autism can return to society and live, study, and work independently [5]. The American National Standard Evidence-Based Practice Guidelines report concludes that there is currently no universally effective treatment for autism spectrum disorder and that a multimodal approach is more likely to promote child development, improve behavior, and reduce child and family stress [6]. At present, the internationally recognized rehabilitation measures for children with autism mainly include interpersonal relationship training, discrete unit teaching method, key skills training, language training, structured teaching, picture exchange communication system, music therapy, sensory integration ability training, functional communication training, and other comprehensive interventions. Reference [7] believes that accurate assessment of the problems of children with autism can play an important role in the early diagnosis and targeted intervention of children with autism. Children with autism may benefit from early discovery, diagnosis, and treatment, which can help alleviate their symptoms [8]. Due to the disadvantages of long-term drug treatment, domestic and foreign child psychologists have begun to focus on the application of nondrug interventions in the treatment of CA, and social psychological interventions are widely used in the treatment of CA, such as family intervention. Family interventions mainly include parental training and systematic family therapy [9]. Among them, parental training has been adopted by CA multimodal therapy research institute and has made great research progress. At present, domestic treatment is also changing to a comprehensive treatment method, but the current social and psychological intervention is relatively chaotic, and a complete comprehensive intervention system has not yet been established. There are also some studies on systematic parenting training in China, but there are still many deficiencies. For example, the eight-step method of parent training by Barkley is simply used, and it has not been properly revised according to the cultural environment of our country. It is easy to fall off; the evaluation of the effect of previous studies has mostly focused on the core symptoms and behavioral problems of children, and less attention has been paid to the psychological conditions of children and parents. In this context, this paper proposes a systematic parent training based on computational intelligence to evaluate and analyze the intervention effect of autistic children. Computational intelligence includes many methods. This paper uses the neural network method to evaluate the intervention effect.The following is a description of the study: the introduction is in Section1, and Section 2 goes over the related studies. Methods of the proposed work are discussed in Section 3. Experimental results and evaluation are covered in Section 4; finally, in Section 5, the conclusion puts the paper to a close.


## 2. Related Work

Autism has received attention since it was first mentioned in 1943, and its definition has been constantly changing as research on such children progresses [10]. Autism is not a single disease, but a spectrum of developmental disorders consisting of several syndromes, and the spectrum of autism is constantly evolving with research. The first is the increase in the number of people covered, from the beginning of childhood autism to the definition of adult autism, making the concept of autism more comprehensive. The second is the continuous updating of connotation and extension, the increasingly clear diagnostic criteria in connotation, and the addition and removal of related syndromes in extension, all of which make the concept of autism more perfect [11]. But in this spectrum, the most important feature is the three core symptoms of autism: social interaction disorder, speech disorder, and behavior disorder. Reference [12] believes that the difference between autism and obsessive-compulsive disorder is that autistic people are immersed in stereotyped behaviors and ritualized behaviors, while obsessive-compulsive patients are accompanied by obsessive-compulsive behaviors in a depressed state; the difference between people with autism and people with language disabilities is that people with language disabilities can use nonverbal communication to compensate for verbal communication, while people with autism have impairments in both verbal and nonverbal communication; the difference between people with autism and people with mental retardation is that people with autism fluctuate greatly in their intellectual development, while people with mental retardation have been in a state of delayed intellectual development. CA, also known as classic autism, is characterized by significantly aberrant or hindered social interaction and communication development, as well as significantly stereotyped behaviors and interests [13]. So far, the treatment methods for children with autism are very diverse: there are play therapy, sensory integration therapy, behavioral therapy, music therapy, art therapy, TEACCH structured teaching, etc., each of which has its own characteristics. In my country, educational intervention and behavioral training have become the mainstream of rehabilitation for CA. Some approaches focus on emotional and interpersonal interactions in CA, while others focus on cognitive and communication development [14]. In the early 20th century, with society’s emphasis on children’s rights and legal status, experts in helping children with emotional disorders launched the child counseling movement. According to the interaction theory of family system, children with autism will not only have an impact on their families, but also, families will act on children with autism and affect the children’s rehabilitation process. Reference [15] research suggests that family parenting style will affect the social ability of children with autism. These studies show that parental emotions, behaviors, and stress can affect children’s development, including social interaction and problem behaviors. Reference [16] conducted a comparative study on 151 families of children with autism and 113 families of ordinary children in a province. The findings suggest that families with autistic children, particularly moms, have higher psychological issues, marriage troubles, and family dysfunction. A study in reference [17] compared the personality traits of parents of autistic children to those of parents of typically developing children and found that the personality traits of parents of autistic children differed considerably from those of parents of typically developing children and they both showed emotional instability and psychotic personality characteristics. Reference [18] used “Parent-Child Interaction Therapy” for high-functioning autistic children aged 5-12 with behavioral problems. The adaptability of children with autism improved, and the positive interaction between children with autism and their parents increased. Reference [19] used the “Family-Implemented Treatment for Behavioral Inflexibility” on 5 autistic children with an average age of 48 months for a 12-week intervention. The results of the study showed that parental participation in the treatment of autistic children can significantly reduce self-esteem. Stereotyped behavior in children with autism was observed, 4 of whom were followed up at 2 or 4 weeks to show that the intervention continued to be effective. It can be seen that parental participation in the treatment of autistic children is conducive not only to the recovery of autistic children but also to the improvement of parents’ mental health. Since the health level of both parents and children in the family system has improved, it can be speculated that involving parents in the rehabilitation training of autistic children can improve the function of the family system. Reference [20] conducted an intervention study on 5 autistic children aged 3-9 years in combination with family therapy. The results showed that family therapy can promote the psychological recovery of autistic children and improve the children’s sensory ability, interpersonal skills, and physical motor skills. References [21–23] conducted an intervention study on 33 autistic children with assisted family therapy. The results showed that the children’s abilities improved in various parts, but the progress was uneven, and communication and language were more significant. Family therapy is changing into a school of therapy based on empirical research, and family therapy is integrating techniques and theories from other schools. This integration trend is not only driven by its internal theoretical integration but also driven by external pressure and social responsibility. Thus, systematic parental training interventions for children with autism may have unexpected positive effects.


## 3. Method

### 3.1. Systematic Training for Parents of Children with Autism

The parent training system refers to New Forest’s “Six-step Method of Parenting Plan” and Barkley’s eight-step method of child behavior management, combined with the family characteristics of autistic children in my country to design a systematic parent training system, including two aspects: child behavior and parent psychology. In each level of training guidance, the main training content is as follows.(1)
Basic knowledge training of CAThe trainer will explain the basic knowledge of CA’s clinical manifestations, pathogenic factors, diagnosis, prognosis, epidemic status, etc., answer parents’ questions about CA, help parents understand the reasons behind these behaviors of CA children, and understand and acceptance of children.(2)
Comprehensive intervention of CAParents express their own views, including the children’s current medication, questions about medication, and desired intervention methods. The trainer introduces the current clinical treatment methods, the effects of treatment methods, and the treatment mechanism of drugs. Help parents understand how to deal with children with CA, and eliminate parents’ confusion and concerns about drug treatment of CA.(3)
Learning management of CA childrenParents express their views and exchange experiences on the reasons for children’s learning difficulties and how to better communicate with school teachers. The trainer explains the causes of CA children’s learning difficulties, learning methods and strategies, behavioral skills for children to manage their own schoolwork, and how to develop a daily behavioral school report card to integrate home-based reward programs with school-based reward programs. On the basis of understanding the children in the first two steps, parents use the academic management skills they have learned to gradually help children solve their academic problems.(4)
Behavior modification of CA childrenParents express their opinions on the methods they have taken to manage children’s bad behavior in the past and share their experiences with each other. The trainer explains the principles, common techniques and methods of behavioral therapy, the principles of establishing behavioral contracts, and how to choose reinforcers. Parents can flexibly apply the behavioral modification methods introduced by the trainer according to the different performances of their children.(5)
Help CA children deal with interpersonal relationshipsCA parents talk to each other about their children’s challenges with interpersonal interactions and social skills that have been covered with them. The trainer explained the types of social skills, how to help children learn to communicate with others, how to improve parent-child relationships, how to adjust the relationship between CA children and teachers and help parents understand the correct way to communicate with CA children, strategies for playing games with CA children, etc. Parents learn to teach their children how to express their emotions and how to deal with their own emotions. Parents can use family reward systems, role-playing, and other methods to help children improve their social skills.(6)
Emotional and stress management of parents of CA childrenParents tell their parenting pressures and negative emotions and share their experiences according to their own adjustment methods. The trainer explained the causes of negative emotions and the management methods and skills of negative emotions and stress. Help parents understand the causes of negative emotions, how to deal with the negative emotions when facing children with CA, how to communicate with teachers reasonably and effectively, and with problem-solving strategies and skills.


### 3.2. Artificial Neural Network Method

Based on the structure and function of the biological brain, ANN is an information-processing system. Neural network theory is an information science that learns from the human brain. It is a field that developed rapidly in the mid- to late 1980s. Its development has had an important impact on computer science, AI, cognitive science, and other fields. A neural network is a network composed of many simple neurons connected to each other. Although the structure and function of each neuron are relatively simple, the behavior of the neural network is not a simple superposition of the behavior of each unit, and the overall dynamic behavior of the network is extremely complex, and it can form a highly nonlinear dynamic system, which can express many complex physical systems, showing the basic characteristics of general complex nonlinear systems and various properties as neural network systems. A wide range of neural network models are available, each of which may be used to explain and mimic distinct parts of the nervous system, such as perceptron, BP network, RBP network, two-way associative memory, Hopfield model, and others are good examples of network models. Approximation of functions, clustering of data, categorization of patterns, and optimization calculations are all possible with these network models. It is used mostly for function approximation, pattern recognition, classification, and data compression, among other things. The BP method or a variant on it is the basis for the majority of neural network models now being used in actual ANN applications.


#### 3.2.1. BP Neural Network

The input, hidden, and output layers of a BPNN (a.k.a. multilayer feedforward neural network) are each separated by a layer of neurons. All neurons in each layer exclusively communicate with those in neighboring layers; there is no link across layers; and each layer does not have a feedback connection to other levels. To create an output response, data is sent from the input layer unit to the hidden layer unit, which processes it before sending it to the output layer unit, where it is further processed. Iteratively tracing the path of the output response and modifying the output response weights and thresholds one layer at a time until it is within acceptable limits might reduce this mistake. The connection weights and thresholds for each layer are continuously computed and corrected until the error reaches the required level, at which point the estimated output response is compared to the anticipated output.Figure1 is the topology diagram of the BPNN. Among them, the input signal is represented by Xi, the output of the hidden layer node is represented by Hj, the output of the output node is represented by Yk, the expected output is Tk, the connection weight of the input node i to the hidden layer node j is Vij, the hidden layer node connection weight from j to the output node k is Wjk, and the given number of samples is N; N1, N2, and N3 are the number of input, hidden layer nodes, and output nodes, respectively. In practical application, BPNN can set up multiple hidden layers according to the needs of the problem.Figure 1
Topological structure diagram of the BP neural network.The main characteristics of BPNN are as follows:(1)
Nonlinear mapping capabilityAny nonlinear continuous function may be approximated with arbitrary accuracy using neural networks. The problems associated with data modeling are highly nonlinear.(2)
Parallel distributed processing methodInformation is distributed and stored and processed in parallel, which makes it highly fault-tolerant and has a fast-processing speed.(3)
Self-adaptive abilityIt can extract regular knowledge from input and output data, memorize it in the weights of the network, and have generalization ability, that is, the ability to apply this set of weights to general situations. Learning of neural networks can also be done online.(4)
The ability of data fusionIt can process quantitative information and qualitative information at the same time and can use traditional engineering technology and AI technology.(5)
Multivariable systemAn unlimited number of input and output variables in a neural network may be used to describe both single-variable and multivariable systems without addressing the issue of decoupling between subsystems.


#### 3.2.2. Disadvantages of the BP Neural Network

In the traditional BP approach, which is a gradient descent algorithm with weights and thresholds moving in the opposite direction as the gradient, which is the direction in which the operation processing function declines the fastest, the Widrow-Hoff learning rule is utilized. The error of BPNN is a function of the weights of each layer and the pair of input samples. Through the analysis of the error surface distribution in its two-dimensional weight space, it can be seen that the BPNN has the following limitations.(1)
There are flat areasSome areas on the error surface are relatively flat. In these areas, the gradient of the error changes very little. Even if the adjustment of the weights is large, the error still decreases slowly. Only when the adjustment direction is correct and the adjustment time is long enough it can exit the flat area and enter a certain valley, thus affecting the convergence speed.(2)
There are multiple minimum pointsIn the BP algorithm, which uses the gradient descent approach for nonlinear optimization, local minima are unavoidable. Because the solution space for real issues is generally a multidimensional surface with multiple local minima, the likelihood of trapping in local minima is considerably increased. Because random weights are often used in the BP method, it is difficult to train the network to its global optimum. As a result, the training cannot converge to the given error.


#### 3.2.3. Improved Algorithm Based on Gradient Descent

When modifying weights, the standard BP technique just considers the error’s gradient descent direction at timet, which can lead to training process oscillation and sluggish convergence. The improved algorithm based on gradient descent is based on the standard gradient descent method; that is, each time the network weights and thresholds are corrected, the correction amount of the previous learning is added according to a certain proportion, thereby accelerating the convergence of network learning. The specific method is
(1)∆Wt+1=c+θΔWt,where Wt+1 is the correction amount that should be obtained this time, Wt is the previous correction amount, c is the correction amount calculated from the current error, and θ is the learning rate.It can be seen from the above formula that if the previous correction amount is overadjusted, the inertia term is opposite to the current error correction term, so that the actual correction amount this time is small, which has the effect of reducing oscillation, while the current correction amount is underadjusted. When the inertia term of this error calculation has the same sign as the correction term, the actual correction amount of this error increases, which helps to speed up the repair.


### 3.3. Child Autism Evaluation Index System

When constructing the evaluation index system of CA, this paper adopts the “Childhood Autism Rating Scale” (CARS), which is for professionals to evaluate, and it is composed of interpersonal relationship, imitation, emotional response, body movement ability, relationship with nonliving objects, adaptability to environmental changes, visual response, auditory response, near-sensory response, anxiety response, verbal communication, nonverbal communication, activity level, intellectual function, and general impression composition; each separate score is 1, 2, 3, and 4. The total score is greater or equal to 30 points as the cut-off point for diagnosing autism in children, that is, level I; the total score is 30-36 points, and less than 5 items are less than 3 points. When the overall score is greater than or equal to 36 points and more than 5 items are over 3 points, it is classified as severe autism, which is grade III. See Table1 for details.Table 1
Childhood autism assessment index system.
IndexScoreLabelInterpersonal relationship1-4X1Imitate1-4X2Emotional response1-4X3Physical ability1-4X4Relationship with inanimate objects1-4X5Adaptability to environmental changes1-4X6Visual response1-4X7Auditory response1-4X8Proximity sensory response1-4X9Anxiety response1-4X10Language communication1-4X11Nonverbal communication1-4X12Activity level1-4X13Intellectual function1-4X14General impression1-4X15


### 3.4. Evaluation Design of Intervention Effect Based on Neural Network

The algorithm flow of the BPNN is shown in Figure2.Figure 2
BP neural network algorithm flow chart.The formula involved in this training process is as follows:(2)Hs=∑i=1nVijxis−tj,where s refers to the number of samples in the sth, H refers to the number of hidden layers, Vij is the connection weight between the ith input layer neuron and the jth hidden layer neuron, and tj refers to the threshold of the jth neuron in the hidden layer of the network.
(3)fx=11+e−x,where x refers to the hidden layer input value and fx is the output function.
(4)yis=∑j=1mWjkHjs−tk,(5)yks=fyis,where Wjk is the connection weight between the jth neuron cell in the innermost hidden layer and the kth output neuron cell, yis is the output function of the sth data sample, tk is the output function of the output layer threshold of the kth neuron cell, and Hj is the value of the Hth neuron cell.
(6)E=0.5∗Tji−yOji,δj2s=Tjs−yOjm∗yOjm∗1−yOjm,δj1s=∑i=1nVij2∗δi2s∗yijs∗1−yijs,E=∑i=1sEis,Vji2=Vji2+λ∗∑l=1sδj2l∗yOil,Wji1=Wji1+λ∗∑l=1sδj1l∗xil,where Tji is the expected output of the jth sample after the neural network training, E is the error value of the output, and yOji is the actual output of the sample.It will not be executed untilE<ε; that is, the network training phase will not be executed until this requirement is met. The information is calculated and sorted according to the neural network model’s learning algorithm principle, and the preprocessed effective information can be utilized to enter the implementation process, with the BPNN model’s learning algorithm used for prediction and testing. The selection of a suitable network model structure plays a prominent role in the overall environment construction. An excellent network model structure can reduce the number of trainings, which is conducive to enhancing work efficiency and reducing engineering workload. The system is a nonlinear mapping from the input of the evaluation criteria to the output of the evaluation criteria. This paper uses a three-layer BPNN, and its advantages are mainly shown in the following: the three-layer BPNN uses a precision approximation to any mapping relationship and can effectively solve some nonlinear problems by using input, implicit, and output. According to the characteristics of children’s autism evaluation indicators, in this topic, the evaluation criteria are divided into 15 secondary indicators, and the corresponding number of input nodes is established for the BP training network. The determination of the number of neurons in the hidden layer does not yet have an accepted standard. Here, a widely used empirical formula is used to calculate the number of hidden layers:
(7)H=m+n+a,where m is the number of nodes in the input layer, n is the number of nodes in the output layer, H is the number of inputs in the hidden layer, and a is a constant from 1 to 10. It can be seen that the number of nodes in the hidden layer is in the range of 5 to 14, and the final number requires to be determined through experimentation.


## 3.1. Systematic Training for Parents of Children with Autism

The parent training system refers to New Forest’s “Six-step Method of Parenting Plan” and Barkley’s eight-step method of child behavior management, combined with the family characteristics of autistic children in my country to design a systematic parent training system, including two aspects: child behavior and parent psychology. In each level of training guidance, the main training content is as follows.(1)
Basic knowledge training of CAThe trainer will explain the basic knowledge of CA’s clinical manifestations, pathogenic factors, diagnosis, prognosis, epidemic status, etc., answer parents’ questions about CA, help parents understand the reasons behind these behaviors of CA children, and understand and acceptance of children.(2)
Comprehensive intervention of CAParents express their own views, including the children’s current medication, questions about medication, and desired intervention methods. The trainer introduces the current clinical treatment methods, the effects of treatment methods, and the treatment mechanism of drugs. Help parents understand how to deal with children with CA, and eliminate parents’ confusion and concerns about drug treatment of CA.(3)
Learning management of CA childrenParents express their views and exchange experiences on the reasons for children’s learning difficulties and how to better communicate with school teachers. The trainer explains the causes of CA children’s learning difficulties, learning methods and strategies, behavioral skills for children to manage their own schoolwork, and how to develop a daily behavioral school report card to integrate home-based reward programs with school-based reward programs. On the basis of understanding the children in the first two steps, parents use the academic management skills they have learned to gradually help children solve their academic problems.(4)
Behavior modification of CA childrenParents express their opinions on the methods they have taken to manage children’s bad behavior in the past and share their experiences with each other. The trainer explains the principles, common techniques and methods of behavioral therapy, the principles of establishing behavioral contracts, and how to choose reinforcers. Parents can flexibly apply the behavioral modification methods introduced by the trainer according to the different performances of their children.(5)
Help CA children deal with interpersonal relationshipsCA parents talk to each other about their children’s challenges with interpersonal interactions and social skills that have been covered with them. The trainer explained the types of social skills, how to help children learn to communicate with others, how to improve parent-child relationships, how to adjust the relationship between CA children and teachers and help parents understand the correct way to communicate with CA children, strategies for playing games with CA children, etc. Parents learn to teach their children how to express their emotions and how to deal with their own emotions. Parents can use family reward systems, role-playing, and other methods to help children improve their social skills.(6)
Emotional and stress management of parents of CA childrenParents tell their parenting pressures and negative emotions and share their experiences according to their own adjustment methods. The trainer explained the causes of negative emotions and the management methods and skills of negative emotions and stress. Help parents understand the causes of negative emotions, how to deal with the negative emotions when facing children with CA, how to communicate with teachers reasonably and effectively, and with problem-solving strategies and skills.


## 3.2. Artificial Neural Network Method

Based on the structure and function of the biological brain, ANN is an information-processing system. Neural network theory is an information science that learns from the human brain. It is a field that developed rapidly in the mid- to late 1980s. Its development has had an important impact on computer science, AI, cognitive science, and other fields. A neural network is a network composed of many simple neurons connected to each other. Although the structure and function of each neuron are relatively simple, the behavior of the neural network is not a simple superposition of the behavior of each unit, and the overall dynamic behavior of the network is extremely complex, and it can form a highly nonlinear dynamic system, which can express many complex physical systems, showing the basic characteristics of general complex nonlinear systems and various properties as neural network systems. A wide range of neural network models are available, each of which may be used to explain and mimic distinct parts of the nervous system, such as perceptron, BP network, RBP network, two-way associative memory, Hopfield model, and others are good examples of network models. Approximation of functions, clustering of data, categorization of patterns, and optimization calculations are all possible with these network models. It is used mostly for function approximation, pattern recognition, classification, and data compression, among other things. The BP method or a variant on it is the basis for the majority of neural network models now being used in actual ANN applications.


### 3.2.1. BP Neural Network

The input, hidden, and output layers of a BPNN (a.k.a. multilayer feedforward neural network) are each separated by a layer of neurons. All neurons in each layer exclusively communicate with those in neighboring layers; there is no link across layers; and each layer does not have a feedback connection to other levels. To create an output response, data is sent from the input layer unit to the hidden layer unit, which processes it before sending it to the output layer unit, where it is further processed. Iteratively tracing the path of the output response and modifying the output response weights and thresholds one layer at a time until it is within acceptable limits might reduce this mistake. The connection weights and thresholds for each layer are continuously computed and corrected until the error reaches the required level, at which point the estimated output response is compared to the anticipated output.Figure1 is the topology diagram of the BPNN. Among them, the input signal is represented by Xi, the output of the hidden layer node is represented by Hj, the output of the output node is represented by Yk, the expected output is Tk, the connection weight of the input node i to the hidden layer node j is Vij, the hidden layer node connection weight from j to the output node k is Wjk, and the given number of samples is N; N1, N2, and N3 are the number of input, hidden layer nodes, and output nodes, respectively. In practical application, BPNN can set up multiple hidden layers according to the needs of the problem.Figure 1
Topological structure diagram of the BP neural network.The main characteristics of BPNN are as follows:(1)
Nonlinear mapping capabilityAny nonlinear continuous function may be approximated with arbitrary accuracy using neural networks. The problems associated with data modeling are highly nonlinear.(2)
Parallel distributed processing methodInformation is distributed and stored and processed in parallel, which makes it highly fault-tolerant and has a fast-processing speed.(3)
Self-adaptive abilityIt can extract regular knowledge from input and output data, memorize it in the weights of the network, and have generalization ability, that is, the ability to apply this set of weights to general situations. Learning of neural networks can also be done online.(4)
The ability of data fusionIt can process quantitative information and qualitative information at the same time and can use traditional engineering technology and AI technology.(5)
Multivariable systemAn unlimited number of input and output variables in a neural network may be used to describe both single-variable and multivariable systems without addressing the issue of decoupling between subsystems.


### 3.2.2. Disadvantages of the BP Neural Network

In the traditional BP approach, which is a gradient descent algorithm with weights and thresholds moving in the opposite direction as the gradient, which is the direction in which the operation processing function declines the fastest, the Widrow-Hoff learning rule is utilized. The error of BPNN is a function of the weights of each layer and the pair of input samples. Through the analysis of the error surface distribution in its two-dimensional weight space, it can be seen that the BPNN has the following limitations.(1)
There are flat areasSome areas on the error surface are relatively flat. In these areas, the gradient of the error changes very little. Even if the adjustment of the weights is large, the error still decreases slowly. Only when the adjustment direction is correct and the adjustment time is long enough it can exit the flat area and enter a certain valley, thus affecting the convergence speed.(2)
There are multiple minimum pointsIn the BP algorithm, which uses the gradient descent approach for nonlinear optimization, local minima are unavoidable. Because the solution space for real issues is generally a multidimensional surface with multiple local minima, the likelihood of trapping in local minima is considerably increased. Because random weights are often used in the BP method, it is difficult to train the network to its global optimum. As a result, the training cannot converge to the given error.


### 3.2.3. Improved Algorithm Based on Gradient Descent

When modifying weights, the standard BP technique just considers the error’s gradient descent direction at timet, which can lead to training process oscillation and sluggish convergence. The improved algorithm based on gradient descent is based on the standard gradient descent method; that is, each time the network weights and thresholds are corrected, the correction amount of the previous learning is added according to a certain proportion, thereby accelerating the convergence of network learning. The specific method is
(1)∆Wt+1=c+θΔWt,where Wt+1 is the correction amount that should be obtained this time, Wt is the previous correction amount, c is the correction amount calculated from the current error, and θ is the learning rate.It can be seen from the above formula that if the previous correction amount is overadjusted, the inertia term is opposite to the current error correction term, so that the actual correction amount this time is small, which has the effect of reducing oscillation, while the current correction amount is underadjusted. When the inertia term of this error calculation has the same sign as the correction term, the actual correction amount of this error increases, which helps to speed up the repair.


## 3.2.1. BP Neural Network

The input, hidden, and output layers of a BPNN (a.k.a. multilayer feedforward neural network) are each separated by a layer of neurons. All neurons in each layer exclusively communicate with those in neighboring layers; there is no link across layers; and each layer does not have a feedback connection to other levels. To create an output response, data is sent from the input layer unit to the hidden layer unit, which processes it before sending it to the output layer unit, where it is further processed. Iteratively tracing the path of the output response and modifying the output response weights and thresholds one layer at a time until it is within acceptable limits might reduce this mistake. The connection weights and thresholds for each layer are continuously computed and corrected until the error reaches the required level, at which point the estimated output response is compared to the anticipated output.Figure1 is the topology diagram of the BPNN. Among them, the input signal is represented by Xi, the output of the hidden layer node is represented by Hj, the output of the output node is represented by Yk, the expected output is Tk, the connection weight of the input node i to the hidden layer node j is Vij, the hidden layer node connection weight from j to the output node k is Wjk, and the given number of samples is N; N1, N2, and N3 are the number of input, hidden layer nodes, and output nodes, respectively. In practical application, BPNN can set up multiple hidden layers according to the needs of the problem.Figure 1
Topological structure diagram of the BP neural network.The main characteristics of BPNN are as follows:(1)
Nonlinear mapping capabilityAny nonlinear continuous function may be approximated with arbitrary accuracy using neural networks. The problems associated with data modeling are highly nonlinear.(2)
Parallel distributed processing methodInformation is distributed and stored and processed in parallel, which makes it highly fault-tolerant and has a fast-processing speed.(3)
Self-adaptive abilityIt can extract regular knowledge from input and output data, memorize it in the weights of the network, and have generalization ability, that is, the ability to apply this set of weights to general situations. Learning of neural networks can also be done online.(4)
The ability of data fusionIt can process quantitative information and qualitative information at the same time and can use traditional engineering technology and AI technology.(5)
Multivariable systemAn unlimited number of input and output variables in a neural network may be used to describe both single-variable and multivariable systems without addressing the issue of decoupling between subsystems.


## 3.2.2. Disadvantages of the BP Neural Network

In the traditional BP approach, which is a gradient descent algorithm with weights and thresholds moving in the opposite direction as the gradient, which is the direction in which the operation processing function declines the fastest, the Widrow-Hoff learning rule is utilized. The error of BPNN is a function of the weights of each layer and the pair of input samples. Through the analysis of the error surface distribution in its two-dimensional weight space, it can be seen that the BPNN has the following limitations.(1)
There are flat areasSome areas on the error surface are relatively flat. In these areas, the gradient of the error changes very little. Even if the adjustment of the weights is large, the error still decreases slowly. Only when the adjustment direction is correct and the adjustment time is long enough it can exit the flat area and enter a certain valley, thus affecting the convergence speed.(2)
There are multiple minimum pointsIn the BP algorithm, which uses the gradient descent approach for nonlinear optimization, local minima are unavoidable. Because the solution space for real issues is generally a multidimensional surface with multiple local minima, the likelihood of trapping in local minima is considerably increased. Because random weights are often used in the BP method, it is difficult to train the network to its global optimum. As a result, the training cannot converge to the given error.


## 3.2.3. Improved Algorithm Based on Gradient Descent

When modifying weights, the standard BP technique just considers the error’s gradient descent direction at timet, which can lead to training process oscillation and sluggish convergence. The improved algorithm based on gradient descent is based on the standard gradient descent method; that is, each time the network weights and thresholds are corrected, the correction amount of the previous learning is added according to a certain proportion, thereby accelerating the convergence of network learning. The specific method is
(1)∆Wt+1=c+θΔWt,where Wt+1 is the correction amount that should be obtained this time, Wt is the previous correction amount, c is the correction amount calculated from the current error, and θ is the learning rate.It can be seen from the above formula that if the previous correction amount is overadjusted, the inertia term is opposite to the current error correction term, so that the actual correction amount this time is small, which has the effect of reducing oscillation, while the current correction amount is underadjusted. When the inertia term of this error calculation has the same sign as the correction term, the actual correction amount of this error increases, which helps to speed up the repair.


## 3.3. Child Autism Evaluation Index System

When constructing the evaluation index system of CA, this paper adopts the “Childhood Autism Rating Scale” (CARS), which is for professionals to evaluate, and it is composed of interpersonal relationship, imitation, emotional response, body movement ability, relationship with nonliving objects, adaptability to environmental changes, visual response, auditory response, near-sensory response, anxiety response, verbal communication, nonverbal communication, activity level, intellectual function, and general impression composition; each separate score is 1, 2, 3, and 4. The total score is greater or equal to 30 points as the cut-off point for diagnosing autism in children, that is, level I; the total score is 30-36 points, and less than 5 items are less than 3 points. When the overall score is greater than or equal to 36 points and more than 5 items are over 3 points, it is classified as severe autism, which is grade III. See Table1 for details.Table 1
Childhood autism assessment index system.
IndexScoreLabelInterpersonal relationship1-4X1Imitate1-4X2Emotional response1-4X3Physical ability1-4X4Relationship with inanimate objects1-4X5Adaptability to environmental changes1-4X6Visual response1-4X7Auditory response1-4X8Proximity sensory response1-4X9Anxiety response1-4X10Language communication1-4X11Nonverbal communication1-4X12Activity level1-4X13Intellectual function1-4X14General impression1-4X15


## 3.4. Evaluation Design of Intervention Effect Based on Neural Network

The algorithm flow of the BPNN is shown in Figure2.Figure 2
BP neural network algorithm flow chart.The formula involved in this training process is as follows:(2)Hs=∑i=1nVijxis−tj,where s refers to the number of samples in the sth, H refers to the number of hidden layers, Vij is the connection weight between the ith input layer neuron and the jth hidden layer neuron, and tj refers to the threshold of the jth neuron in the hidden layer of the network.
(3)fx=11+e−x,where x refers to the hidden layer input value and fx is the output function.
(4)yis=∑j=1mWjkHjs−tk,(5)yks=fyis,where Wjk is the connection weight between the jth neuron cell in the innermost hidden layer and the kth output neuron cell, yis is the output function of the sth data sample, tk is the output function of the output layer threshold of the kth neuron cell, and Hj is the value of the Hth neuron cell.
(6)E=0.5∗Tji−yOji,δj2s=Tjs−yOjm∗yOjm∗1−yOjm,δj1s=∑i=1nVij2∗δi2s∗yijs∗1−yijs,E=∑i=1sEis,Vji2=Vji2+λ∗∑l=1sδj2l∗yOil,Wji1=Wji1+λ∗∑l=1sδj1l∗xil,where Tji is the expected output of the jth sample after the neural network training, E is the error value of the output, and yOji is the actual output of the sample.It will not be executed untilE<ε; that is, the network training phase will not be executed until this requirement is met. The information is calculated and sorted according to the neural network model’s learning algorithm principle, and the preprocessed effective information can be utilized to enter the implementation process, with the BPNN model’s learning algorithm used for prediction and testing. The selection of a suitable network model structure plays a prominent role in the overall environment construction. An excellent network model structure can reduce the number of trainings, which is conducive to enhancing work efficiency and reducing engineering workload. The system is a nonlinear mapping from the input of the evaluation criteria to the output of the evaluation criteria. This paper uses a three-layer BPNN, and its advantages are mainly shown in the following: the three-layer BPNN uses a precision approximation to any mapping relationship and can effectively solve some nonlinear problems by using input, implicit, and output. According to the characteristics of children’s autism evaluation indicators, in this topic, the evaluation criteria are divided into 15 secondary indicators, and the corresponding number of input nodes is established for the BP training network. The determination of the number of neurons in the hidden layer does not yet have an accepted standard. Here, a widely used empirical formula is used to calculate the number of hidden layers:
(7)H=m+n+a,where m is the number of nodes in the input layer, n is the number of nodes in the output layer, H is the number of inputs in the hidden layer, and a is a constant from 1 to 10. It can be seen that the number of nodes in the hidden layer is in the range of 5 to 14, and the final number requires to be determined through experimentation.


## 4. Experiment and Analysis

In this section, data set, parameter experiment, and evaluation accuracy of the BP network model are discussed in detail.


### 4.1. Data Set and Parameter Experiment

In order to facilitate the experiment, this paper designs a data set based on the evaluation index system of CA, with a total of 1200 sets of data, including 1000 training sets and 200 test sets. According to the formula of the hidden layer, it can be determined that the number of nodes in the hidden layer is in the range of 5 to 14. Therefore, this paper selects the number of nodes to be 6, 8, 10, 12, and 14 for experiments. The experimental results are shown in Figures3–5.Figure 3
Training effect whenN=6 and N=8.Figure 4
Training effect whenN=10 and N=12.Figure 5
Training effect whenN=14.The training impact is optimal when the number of hidden layer nodes is 12, according to the experimental results; hence, the number of hidden layer nodes picked is 12.


### 4.2. Evaluation Accuracy of the BP Network Model

The normalized data is used to conduct experiments, and the obtained results are compared with the evaluation results of experts, as shown in Table2.Table 2
Comparison of network model output and expert evaluation results.
Number12345678Network output0.710.680.850.890.680.740.810.79Expert assessment0.710.690.860.900.680.750.810.78From the experimental results, the prediction results of the model are very close to the evaluation results of experts, and the error is small, which shows that the BP network model proposed in this paper has good performance.


## 4.1. Data Set and Parameter Experiment

In order to facilitate the experiment, this paper designs a data set based on the evaluation index system of CA, with a total of 1200 sets of data, including 1000 training sets and 200 test sets. According to the formula of the hidden layer, it can be determined that the number of nodes in the hidden layer is in the range of 5 to 14. Therefore, this paper selects the number of nodes to be 6, 8, 10, 12, and 14 for experiments. The experimental results are shown in Figures3–5.Figure 3
Training effect whenN=6 and N=8.Figure 4
Training effect whenN=10 and N=12.Figure 5
Training effect whenN=14.The training impact is optimal when the number of hidden layer nodes is 12, according to the experimental results; hence, the number of hidden layer nodes picked is 12.


## 4.2. Evaluation Accuracy of the BP Network Model

The normalized data is used to conduct experiments, and the obtained results are compared with the evaluation results of experts, as shown in Table2.Table 2
Comparison of network model output and expert evaluation results.
Number12345678Network output0.710.680.850.890.680.740.810.79Expert assessment0.710.690.860.900.680.750.810.78From the experimental results, the prediction results of the model are very close to the evaluation results of experts, and the error is small, which shows that the BP network model proposed in this paper has good performance.


## 5. Conclusion

It has been discovered that in the rehabilitation training of autistic children, the role of parents and family environment should be taken into consideration. Children’s first teachers are their parents, and their primary living environment is their family. Family members spend a lot of time with children with autism. Parents are the persons who have the greatest interaction with their children, as well as the people who are closest to and most trusted by them. The cooperation of family members can significantly improve the effect of rehabilitation training for children with autism. Parents are also often involved in therapy and continue to look for other interventions, but a mixed approach or taking an eclectic approach can backfire and interfere with rehabilitation progression, especially if it is unsupported by evidence. Research has found that parents raising an autistic child are more likely to experience greater stress and mental health problems than those raising a child with other disabilities. It is crucial that parents receive systematic training and act as partners in the development of the intervention plan. According to some studies, providing parents of autistic children with an educational program that focuses on increasing their children’s self-efficacy and educational approaches while also addressing their children’s behavioral issues, functioning communication, and adaptive behavior can be beneficial. To this end, this paper has completed the following work: firstly, the harm caused by CA and the current domestic and foreign researches on using different treatment methods to intervene in autistic children are introduced, focusing on the research on family therapy intervention in autistic children. Second, a portion of the systematic training for parents of autistic children is discussed, followed by an explanation of the structure and principles of BPNN. Finally, the BPNN is utilized to create a model for assessing the intervention effect of systematic parent training on autistic children. The experimental results show that the BPNN proposed in this paper has a high accuracy rate.



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*Source: 7746374-2022-06-08.xml*