# Foreign Trade Export Forecast Based on Fuzzy Neural Network

Authors: Yang Liu
Journal: Complexity
 (2021)

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## Abstract

This article first analyzes the significance and methods of foreign trade export forecasting and determines the index system of foreign trade export forecasting by analyzing the results of foreign trade export forecasting research at home and abroad. Subsequently, the related concepts and principles of artificial neural network and fuzzy theory are explained, the types and training algorithms of the fuzzy neural network are introduced, and the neural network and fuzzy theory are combined to establish the prediction model. Finally, according to the characteristics of foreign trade exports, this article comprehensively considers the influence of various factors, applies the fuzzy neural network model to the foreign trade export forecast, introduces the whole process of the establishment of the fuzzy neural network forecasting model in detail, and predicts the change interval of foreign trade exports.


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## Body

## 1. Introduction

Export trade is a complex system with multiple factors, nonlinearities, and ambiguities. There are many factors that affect export trade, including natural resource factors, employment factors, environmental factors, technical factors, and institutional factors, which makes the export trade system appear as a dynamic, nonlinear, and complex system [1]. Coupled with the ambiguity of the movement of factors inside and outside the system, export trade fluctuates around its overall trend during its own operation, and it is difficult to make reliable and accurate predictions. Some researchers believe that the foreign trade environment has undergone major changes, which will inevitably lead to distortions in the model built on the original data structure, and the solution is to make it nonlinear and obscure [2]. Good forecasting results depend to a large extent on the forecasting methods and techniques used. Relevant researchers have done a lot of research work on such a very difficult topic as a prediction. These research works are mainly concentrated in two aspects: on the one hand, they constantly use new theories to explore new forecasting methods and their applications; on the other hand, they use computer and artificial intelligence technology combined with prediction technology to research and develop an intelligent prediction support system, so that ordinary people can use it to make predictions conveniently [3, 4]. Linear models are difficult to grasp the nonlinear phenomena in the export trade system, which will inevitably lead to increased forecast errors. Improved linear models, such as the establishment of piecewise linear models or linear models with time-varying parameters, often give unsatisfactory results. In this case, people are forced to find a nonlinear tool for foreign trade export forecast modeling. In recent years, with the improvement of computer processing power and the development of optimization theory, some nonlinear methods with artificial intelligence have been proposed to be applied to foreign trade export forecasting. Among them, the neural network method can approximate the nonlinear function with arbitrary precision which has been widely used. A large number of experiments have proved that neural networks are indeed much more accurate than traditional measurement methods for foreign trade export forecasts, but neural networks have their own shortcomings that are difficult to overcome: they are easy to fall into local optimum and require a large number of training samples and a large number of repeated experiments. There is no clear standard for the setting technology of control parameters, so there are still some defects when applying neural networks to forecasting foreign trade exports. The fuzzy neural network prediction method is an important content in modern soft computing concepts. Fuzzy systems imitate human logical reasoning ability to deal with problems that are difficult to solve by conventional mathematical methods [5–7]. They can better represent the empirical knowledge and qualitative knowledge described in language, but they usually do not have the ability to learn. They can only subjectively choose membership functions and fuzzy rules; it is difficult to generate a rule set when encountering some complicated problems. The neural network technology is mainly characterized by nonlinear large-scale parallel processing and has strong adaptive self-learning capabilities and direct data processing capabilities. However, the knowledge expression method inside the network is unclear, and the result of its learning depends entirely on the training samples. We combine the neural network with the fuzzy system, initialize the preexpert knowledge in the form of fuzzy rules, and use the neural network learning algorithm to train the fuzzy system to realize the inference process.According to statistics, there are currently more than 300 predictive models and methods used in various control fields. However, because the factors affecting foreign trade are complex and exhibit a high degree of nonlinearity and ambiguity, it is difficult to improve the forecast accuracy of various forecasting methods. However, due to the huge role of foreign trade in promoting national economic development, people have tried to apply mature forecasting models in other fields to the field of foreign trade forecasting and developed a variety of forecasting models and methods. The main prediction methods are as follows: Autoregressive Moving Average (ARIMA) model, regression analysis prediction, gray system model (GM), support vector machine (SVM), neural network-based methods, and combined prediction methods. ARIMA model is a commonly used random time series model, created by Box and Jenkins, so it is also called the B-J method. It is a time series short-term forecasting method with high accuracy. The principle rule is that time series are a set of random variables which depend on time. Although the individual sequence values that constitute the time series are vague, the changes in the entire sequence have certain regularity, and we only have a corresponding mathematical model approximate description. Through the analysis and research of this mathematical model, we can understand the structure and characteristics of the time series more essentially and achieve the optimal prediction in the sense of the smallest variance. This model is mainly used for forecasting, and it can only be used for short-term forecasting. Long-term errors are relatively large. Regression analysis prediction [8] is to establish a regression equation between variables based on the analysis of the correlation between independent variables and dependent variables and use the regression equation as a prediction model, according to the number of independent variables in the prediction period, to predict the dependent variable.This article will construct an index system for export trade forecasting from different perspectives and then apply the fuzzy neural network method to construct an export trade forecasting model. In Section2, through the optimization of the model, the influence of parameters on the model during the training of the fuzzy neural network is discussed. In Section 3, we compare the comparative analysis with common export trade forecasting methods (such as ARIMA, regression analysis, gray system, support vector machine, and neural network methods), and the effectiveness and superiority of fuzzy neural network methods in export trade forecasting are tested. In the last part, the research in this article enriches and expands the current research content of export trade forecasting, and its conclusions can provide a scientific reference for the government to formulate export policies.


## 2. Related Work

In a representative domestic study, Singh [9] believes that, in a large country, economic growth is mainly driven by the expansion of domestic supply and demand rather than changes in foreign factors, but it does not mean that import and export trade can be ignored. It has an important role in the economy and believes that the impact of import and export trade on economic development is mainly reflected in the quality of economic growth and structural transformation and upgrading; it is reflected in maintaining the balance of international payments and maintaining the stability of the RMB exchange rate. Wang et al. [10] believe that when examining the effect of import and export trade on economic growth, both the net export analysis method and the total export volume method have limitations in handling extreme imports. They are not as reasonable and objective as the import decomposition analysis method, and the import and export data from 2000 to 2010 were verified, and the following conclusions were drawn: when import and export trade grew rapidly and exports grew faster than imports, import and export trade had a significant positive effect on economic growth; while import and export trade grew when imports are relatively slow and imports grow faster than exports, import and export trade will have a negative effect on economic growth. Ren et al. [11] started with the analysis of the relationship between my country’s import and export trade and economic growth in their research and concluded from the theoretical and empirical aspects that the scale of my country’s foreign trade exports is not as fast as possible but must obey the development requirements of the macroeconomic goals and maintain the conclusion of the appropriate scale of development of foreign trade. The purpose of economic forecasting is to obtain accurate and reasonable forecast results and use the forecast results as a reference for decision-making. Forecasting includes qualitative forecasting and quantitative forecasting, and quantitative forecasting can be divided into linear forecasting and nonlinear forecasting. Traditional quantitative forecasting methods for economic data include linear forecasting methods such as time series forecasting and linear regression forecasting. When traditional linear forecasting methods become more and more difficult to solve the increasingly complex and changeable economic forecasting problems, nonlinear forecasting methods have emerged, such as neural networks, genetic algorithms, and other emerging nonlinear forecasting methods. Among them, neural network theory has a wide range of applications in the field of economic management, and many studies have proved that neural network models have comparative advantages for predictive decision-making problems in the field of economic management.Alyousifi et al. [12] applied the two learning methods of supervised learning and supervised learning to their respective representative types of neural networks: BP network and Kohonen Self-Organizing Feature Map, two learning paradigms established. The two established learning paradigms were tested using sample data from North Korean listed companies. Through prediction accuracy, discriminant analysis, and Logistic decline value comparison, it was concluded that the BP network is better than the Kohonen self-organizing network. When it is large, the neural network has good prediction accuracy, and when the sample is small, the neural network still has a good prediction effect. Galeshchuk [13] believed that the forecasting effect of export sales is affected by direct and indirect factors. These factors in turn are export sales forecasting commissions, export sales forecasting resources, enterprise export reputation, and export sales forecasting risks. Combining the special characteristics of enterprise organization and export, a path model of export sales forecasting behavior was established, and the performance of the model was verified by example using sample data of UK exporters. The model has good suitability, and the results also show that the key factors affecting the effect of export forecasting are the entrusted items and resources of the enterprise organization. Papageorgiou and Poczęta [14] improved the standard BP algorithm in their research and designed a three-term backpropagation algorithm. The three-term backpropagation algorithm is used in most circumstances, it can be applied to neural networks with different activation functions, but the disadvantage is that when the algorithm satisfies conditions 38 and 39, the system is stable and converges to the local minimum, and when the maximum value of the eigenvalue is quite large, condition 39 may be disturbed. In most cases, if a maximum value of the eigenvalue is denied, the system becomes unstable. In his research, neural network theory is used to solve the forecasting decision-making problems in financial market forecasting, financial management, stock market forecasting, financial accounting, and foreign exchange forecasting economic management. Qiao et al. [15] used neural networks in their research on nonlinear problems in the field of economic management, product quotation decision-making problems, GDP prediction, and RMB real effective exchange rate prediction problems, which have a good prediction or decision-making effects.Judging from the current research status at home and abroad, there are many research results on forecasting and decision-making problems in the field of economic management, which are the hotspots of research, which are inseparable from the high level of social and economic development today. Among various forecasting methods, the theory and knowledge of neural networks have a wide range of applications, such as GDP forecasts, sales forecasts, international trade forecasts, product cost pricing decisions, risk forecasts, stock market forecasts, financial warnings, financial market forecasts, and foreign exchange forecasts. From the above literature, we know that the neural network model has a good fitting effect and prediction accuracy for solving the nonlinear forecasting and decision-making problems in the economic management field and is suitable for the forecasting and decision-making problems in the economic management field.Neural network technology is mainly characterized by nonlinear large-scale parallel processing and has strong adaptive self-learning capabilities and direct data processing capabilities. The shortcomings of the neural network model are as follows. The internal knowledge expression method is not clear, and the result depends entirely on the training samples. The fuzzy model imitates human logical reasoning ability to deal with problems that are difficult to solve by conventional mathematical methods. It can better represent the empirical knowledge and qualitative knowledge described in language but usually does not have the ability to learn and can only choose the membership function subjectively. Concerning fuzzy rules, it is difficult to generate a rule set when encountering complex problems. This paper combines the neural network with the fuzzy system, initializes the preexpert knowledge in the form of fuzzy rules, and trains the fuzzy system with the learning algorithm of the neural network to realize the inference process.


## 3. Forecast Model Based on Fuzzy Neural Network

### 3.1. Fuzzy Neural Network Model

The fuzzy neural network is a new network system produced by the combination of neural network and fuzzy theory. One of its distinctive features is that each node can express fuzzy concepts. Another feature is that as a network, the system always involves fuzzy theory or all or part of it. Zahid et al. [16] published an article “Fuzzy Sets and Neural Networks” in Cybernetics magazine, linking fuzzy sets with neural networks for the first time; in 1975, they published “Fuzzy Sets and Neural Networks” in the magazine Math. Biosci. The article “Fuzzy Neural Networks” clearly studies fuzzy neural networks. In the article, the author generalizes the McCulloch-Pitts neural network model with an intermediate value between 0 and 1. In the next period of time, since the research on neural networks is still at a low ebb, little progress has been made in this area. Chai and Lim [17] proposed a combination of fuzzy membership function and perceptron algorithm. Chen et al. [18] also proposed a new fuzzy neuron. Each input of the new fuzzy neuron does not have a single weight coefficient but a series of fuzzy weight coefficients and real weight coefficients. In the same year, Khosravi et al. also proposed [19] a new fuzzy neuron that is similar to the fuzzy neuron. Liu et al. [20] proposed to use a single fuzzy weight coefficient of a fuzzy neuron for fuzzy control and process learning. In this year, Zhang et al. [21] proposed fuzzy neurons with real weight coefficients, fuzzy thresholds, and fuzzy inputs. Panapakidis and Dagoumas [22] proposed a variety of fuzzy neuron models. Among these models, there are fuzzy neuron models similar to the above, as well as fuzzy neurons that contain fuzzy weight coefficients and can input fuzzy quantities. Typical fuzzy neural networks are BP fuzzy neural network, adaptive neuro-fuzzy inference system (ANFIS), B-spline fuzzy neural network, RBF fuzzy neural network, fuzzy cerebellar model neural network (FCMAC), random fuzzy neural network (SFNN), and wavelet fuzzy neural network. The fuzzy neural network model of this paper is shown in Figure 1.Figure 1
Fuzzy neural network model.


### 3.2. Fuzzy Neural Network Based on T-S Model

In a fuzzy system, there are two main ways to represent a fuzzy model: one is a fuzzy set whose output is a fuzzy set of rules, which is called a fuzzy rule-based neural network based on Mamdani; the other is a fuzzy set of rules as the input function of language variables is typically a linear combination of input variables. Since this model representation was first proposed by Ai et al. [23], it is usually called the T-S model of the fuzzy system, also known as the fuzzy neural network based on the T-S model. The neural network structure based on the T-S fuzzy system is as follows.Suppose inputa=a1,a2,w,an; each component is a fuzzy language variable. Let the set of language variable values ai be(1)fai=xi1,xi2,w,xin.In the formula,xiBi, Bi=1,2,…,m, ai is the first linguistic variable value of Bi, which is defined on the ai fuzzy set. The corresponding membership function is gxiai.Suppose the output vectorb=b1,b2,w,bn; then, the fuzzy rule form of the T-S model is a1∈Xi1,a2∈Xi2,…,an∈Xin.(2)b1j=λ0λ1λ2λwλn1a1a2wan,(3)bkj=λ0kλ1kλ2kλwkλnk1a1a2wan,among them, k=1,2,w,m.If the input quantity adopts the fuzzification method of a single point fuzzy set, for a given inputx, the applicability of each rule is(4)ui=gxi1ai1gxi2ai2wgxinain.The output of the fuzzy system is the weighted average of the output of each rule, namely,(5)bkj=limn⟶∞∑j=1nxjbkjlimn⟶∞∑j=1nxj+1.


## 3.1. Fuzzy Neural Network Model

The fuzzy neural network is a new network system produced by the combination of neural network and fuzzy theory. One of its distinctive features is that each node can express fuzzy concepts. Another feature is that as a network, the system always involves fuzzy theory or all or part of it. Zahid et al. [16] published an article “Fuzzy Sets and Neural Networks” in Cybernetics magazine, linking fuzzy sets with neural networks for the first time; in 1975, they published “Fuzzy Sets and Neural Networks” in the magazine Math. Biosci. The article “Fuzzy Neural Networks” clearly studies fuzzy neural networks. In the article, the author generalizes the McCulloch-Pitts neural network model with an intermediate value between 0 and 1. In the next period of time, since the research on neural networks is still at a low ebb, little progress has been made in this area. Chai and Lim [17] proposed a combination of fuzzy membership function and perceptron algorithm. Chen et al. [18] also proposed a new fuzzy neuron. Each input of the new fuzzy neuron does not have a single weight coefficient but a series of fuzzy weight coefficients and real weight coefficients. In the same year, Khosravi et al. also proposed [19] a new fuzzy neuron that is similar to the fuzzy neuron. Liu et al. [20] proposed to use a single fuzzy weight coefficient of a fuzzy neuron for fuzzy control and process learning. In this year, Zhang et al. [21] proposed fuzzy neurons with real weight coefficients, fuzzy thresholds, and fuzzy inputs. Panapakidis and Dagoumas [22] proposed a variety of fuzzy neuron models. Among these models, there are fuzzy neuron models similar to the above, as well as fuzzy neurons that contain fuzzy weight coefficients and can input fuzzy quantities. Typical fuzzy neural networks are BP fuzzy neural network, adaptive neuro-fuzzy inference system (ANFIS), B-spline fuzzy neural network, RBF fuzzy neural network, fuzzy cerebellar model neural network (FCMAC), random fuzzy neural network (SFNN), and wavelet fuzzy neural network. The fuzzy neural network model of this paper is shown in Figure 1.Figure 1
Fuzzy neural network model.


## 3.2. Fuzzy Neural Network Based on T-S Model

In a fuzzy system, there are two main ways to represent a fuzzy model: one is a fuzzy set whose output is a fuzzy set of rules, which is called a fuzzy rule-based neural network based on Mamdani; the other is a fuzzy set of rules as the input function of language variables is typically a linear combination of input variables. Since this model representation was first proposed by Ai et al. [23], it is usually called the T-S model of the fuzzy system, also known as the fuzzy neural network based on the T-S model. The neural network structure based on the T-S fuzzy system is as follows.Suppose inputa=a1,a2,w,an; each component is a fuzzy language variable. Let the set of language variable values ai be(1)fai=xi1,xi2,w,xin.In the formula,xiBi, Bi=1,2,…,m, ai is the first linguistic variable value of Bi, which is defined on the ai fuzzy set. The corresponding membership function is gxiai.Suppose the output vectorb=b1,b2,w,bn; then, the fuzzy rule form of the T-S model is a1∈Xi1,a2∈Xi2,…,an∈Xin.(2)b1j=λ0λ1λ2λwλn1a1a2wan,(3)bkj=λ0kλ1kλ2kλwkλnk1a1a2wan,among them, k=1,2,w,m.If the input quantity adopts the fuzzification method of a single point fuzzy set, for a given inputx, the applicability of each rule is(4)ui=gxi1ai1gxi2ai2wgxinain.The output of the fuzzy system is the weighted average of the output of each rule, namely,(5)bkj=limn⟶∞∑j=1nxjbkjlimn⟶∞∑j=1nxj+1.


## 4. Foreign Trade Export Forecast Model

Prediction is the basis of decision-making. Scientific and reasonable predictions can not only predict the future situation but also provide a strong basis for decision-making. In recent years, urban export trade has developed rapidly. From 2015 to 2019, the average annual growth rate of urban export trade was 20.32%, maintaining a relatively high level of growth. In 2015, export trade stimulated the economic growth of urban areas by 10%. The data show that export trade has become an important part of the urban economy, driving the urban economy in line with international standards and promoting urban economic development. Therefore, accurate forecasting of urban export trade has important practical economic significance. Export trade is affected by a variety of domestic and international factors and has the characteristics of time-varying, complex, random, and regional characteristics. The export trade of each region has different characteristics and impact indicators because of its own environment and conditions. According to the unique characteristics of urban export trade, this thesis puts forward a macro index system that affects urban export trade, paving the way for a comprehensive study of the urban export trade system.


### 4.1. The Impact Index System of Urban Export Trade

The factors affecting export trade can be divided into domestic and international factors according to different classification standards: economic and political factors, conventional and sudden factors, and other classification factors. Ai et al. [23] in their research paper divided the main factors affecting my country’s foreign trade exports into four categories and 53 variables according to their own country, trading partners, and competitors, but they did not specify the 53 variables. Alam [24] believed that international market demand is the basis for export growth, and technological progress, price advantages, and trade policies are important factors that promote export growth. Alameer et al. [25] directly selected 10 influencing factors such as fixed asset investment in the whole society, total consumption, exchange rate, and foreign direct investment as independent variables for analyzing national export trade. Bas et al. [26] divided the factors that affect foreign trade import and export into 10 indicators in five categories: domestic environmental factors, direct-acting factors, external environmental factors, terms of trade factors, and basic preparation factors. The impact indicator system of urban export trade is shown in Figure 2.Figure 2
The impact indicator system of urban export trade.Existing research mainly studies the impact indicators of my country’s foreign trade imports and exports but does not propose a comprehensive and systematic indicator system that affects the urban export trade system. With the development of the economy and my country’s accession to the WTO, the international and domestic situation has changed. Therefore, my country’s export trade system has new characteristics, and the impact indicators of export trade have also changed accordingly. Due to the different characteristics of urban export trade, the impact indicators are different. According to the special geographical environment, congenital conditions, and preferential policy environment of the urban export trade system, this paper divides the macroeconomic factors that affect urban export trade into five major factors: national macroeconomic factors, regional economic factors, regional natural conditions, policy factors, and international economic factors. The national macroeconomic factors mainly include the national economic growth of the country and other regions, the export trade volume of the country and other regions, the consumption index and household savings of the country and other regions, and the industrial production index of the country and other regions. The economic factors of urban areas mainly include influencing indicators such as national economic growth in urban areas, regional industrial production index and output value, consumption index, household savings, and foreign capital utilization. The natural conditions of urban areas mainly include influencing indicators such as the comprehensive value of the surrounding environment and economy of the city, the throughput of regional ports, the comprehensive value of highway and railway routes, the concentration of natural resources, and labor costs. Policy factors mainly include influencing indicators such as export tariffs, exchange rates, and import and export incentive policies. In this paper, the above four types of indicators are collectively referred to as domestic macro factors. International economic factors mainly include international economic growth, total international imports and exports, comprehensive industrial index of various countries, labor costs of various countries, bilateral trade relations between countries and my country, and nontariff trade barriers.


### 4.2. Selection of Experimental Data

According to the established indicator system, the data collected on the export value and impact indicators of urban mechanical and electrical products are shown in Figure3. The data sources are the State Administration of Taxation, Customs, Statistical Yearbook, and City Statistics Yearbook. The export encouragement policy is a vague factor. We use the expert scoring method to determine its degree of membership.(6)ai=0.85,great effect,0.35,obvious effect,0.15,small effect.Figure 3
Raw data of impact indicators.


### 4.3. Data Preprocessing

The data preprocessing steps are as follows: the purpose of data inspection is to test the correctness, completeness, and consistency of statistics. The completeness and correctness of the data are essential. In addition, the consistency of the statistical caliber is also very important. If the data of some samples are taken from other calibers, it will have an impact on the relationship between the analysis indicators. Due to the different dimensions and value ranges of the data, sometimes the values of different indicators are very different. When solving problems, often large indicators have a large impact on the results, while factors with smaller values and higher importance may play a role. The effect is small. In order to avoid this situation, it is often necessary to normalize the data to change all the parameters to the same range. In data mining technology, data standardization is an important part of data preprocessing, which is a type of data transformation, which is used to transform data into a form suitable for mining processing.Data standardization, also called data standardization, refers to scaling attribute data to a small specific interval. For classification algorithms involving distance measurement, such as support vector machines and neural network algorithms, the standardization of data is particularly useful. The standardization of input values for the attribute measurement of training samples will help speed up machine learning. Standardization of the distance-based method can prevent the criterion with a larger initial domain from being overweighted compared to the criterion with a smaller initial domain, which will affect the prediction effect. Before the fuzzy neural network learning and training, the input and output raw data should be standardized preprocessing.Data preprocessing generally has different calculation formulas according to the difference in the correlation between input and output variables and the purpose of preprocessing. In this paper, a linear function is used to perform standardized preprocessing on the original data. The result of the preprocessing maps the original data to the interval range of [0, l]. Generally, a better prediction effect can be obtained when the standardized sequence is distributed in the interval of [0.1, 0.9]. Therefore, in order to normalize the original data to this interval as much as possible, this paper analyzes the minimum and the maximum scaled appropriately; the standardized data sample set is shown in Figure4.Figure 4
Standardized data sample set.


### 4.4. Correlation Analysis

The data analysis tools used are used to analyze the correlation between the respective indicators and the export trade volume, as shown in Figure5.Figure 5
Correlation between export value and influencing factors.The threshold for screening indicators based on the correlation coefficient is set to 0.5, and those factor variables that have a correlation coefficient less than 0.5 with the target variable are eliminated. At this time, the remaining input indicators are the actual use of foreign capital, the year-end balance of RMB savings deposits of urban and rural residents, the total investment in fixed assets, and the GDP. 10 indicators include the contribution of industrial industry to the growth of regional GDP, scientific expenditure, the purchase price index of raw materials, fuel and power, the exchange rate of US dollar to RMB, and the general consumer price index. As Figure5 reflects only a simple correlation between the dependent variable and the independent variable, the width of the threshold is set to 0.1 to allow more indicators that may hide important information to enter the model and the final indicator system. The 10 indicators of the country are used as input for foreign trade export forecasts.


### 4.5. Fuzzy Neural Network Forecasting Model for Forecasting Foreign Trade Exports

According to the calculation steps of the hybrid fuzzy neural network prediction model proposed in this article, the export volume of urban electromechanical products is predicted. The training parameters of the network are determined through multiple trials, and we choose the number of fuzzy rules to be 5. There are 10 input nodes in the network, which represent various influencing factors and normalize the input data according to formula (2). There is one output node of the network, and the prediction result of the model is calculated inversely according to formula (2) to obtain formula (3), and the prediction result of the same measurement standard as the original data is obtained.The prediction result of the fuzzy neural network model shows that the training sample has a high prediction accuracy under the control of the square error, which is basically within the range of 1%. The training sample prediction result has high accuracy and the error is within 5%, indicating that the export trade volume is in the model. The following has a good forecasting effect. The forecasted data basically fits the actual data of the export trade volume shown in Table1, indicating that the fuzzy neural network forecasting model for the export trade volume is reasonable and usable. The error distribution is shown in Figure 6.Table 1
Forecast value and error of export of mechanical and electrical products.
Actual valueEstimated valueStandard error201533.4133.02−0.015201645.2346.310.002201757.3256.830.034201858.4260.250.042201962.4364.150.025Figure 6
Forecast value and error of export of mechanical and electrical products.It can be seen from Figure6 that the fuzzy neural network model has a better fitting effect for urban export forecasts, with an average error of less than 1%; in particular, the forecast error from 2015 to 2019 is lower than 0.8%, which is relatively large. For example, the Ministry of Foreign Trade and Economic Cooperation implemented the export contract management responsibility system for its specialized foreign trade corporations, and the municipality was directly under the jurisdiction of the central government. However, the limited selection of data samples made foreign trade exports. The changes brought about by policy changes were not fully manifested, and a certain delay occurred. Nevertheless, it precisely reflects the influence of ambiguity factors (changes in export incentive policies) on modeling.


### 4.6. Analysis and Comparison of Forecast Results

As shown in Figure7, we further handle the relationship between quantitative forecasting and decision analysis and establish a feedback mechanism. The quantitative analysis results of the model should serve for decision-making support. Therefore, on the basis of the predicted results, we can further discuss how to formulate reasonable policies and measures to enhance the ability of enterprises to compete internationally for export products and promote the development of export trade. This article uses the correlation analysis of the data growth rate to screen the indicators, and the results are good, but it can be combined with other index reduction methods (such as rough sets) for in-depth research to further optimize the structure of the model. As far as the fuzzy neural network is concerned, the key to constructing the model is the acquisition of fuzzy rules. There is currently no better solution to the redundancy problem of fuzzy rules. Therefore, the algorithm for generating fuzzy rules can be further studied.Figure 7
Comparison of model predictions.It can be seen from the comparative analysis of the results obtained in Figure8 that the prediction results obtained by using fuzzy neural network for prediction analysis fit the actual situation better; MSE and MAPE have a greater degree of reduction compared with other prediction methods, indicating that the method is an effective and feasible method for foreign trade forecasting.Figure 8
Comparison of prediction effects of various prediction methods.


## 4.1. The Impact Index System of Urban Export Trade

The factors affecting export trade can be divided into domestic and international factors according to different classification standards: economic and political factors, conventional and sudden factors, and other classification factors. Ai et al. [23] in their research paper divided the main factors affecting my country’s foreign trade exports into four categories and 53 variables according to their own country, trading partners, and competitors, but they did not specify the 53 variables. Alam [24] believed that international market demand is the basis for export growth, and technological progress, price advantages, and trade policies are important factors that promote export growth. Alameer et al. [25] directly selected 10 influencing factors such as fixed asset investment in the whole society, total consumption, exchange rate, and foreign direct investment as independent variables for analyzing national export trade. Bas et al. [26] divided the factors that affect foreign trade import and export into 10 indicators in five categories: domestic environmental factors, direct-acting factors, external environmental factors, terms of trade factors, and basic preparation factors. The impact indicator system of urban export trade is shown in Figure 2.Figure 2
The impact indicator system of urban export trade.Existing research mainly studies the impact indicators of my country’s foreign trade imports and exports but does not propose a comprehensive and systematic indicator system that affects the urban export trade system. With the development of the economy and my country’s accession to the WTO, the international and domestic situation has changed. Therefore, my country’s export trade system has new characteristics, and the impact indicators of export trade have also changed accordingly. Due to the different characteristics of urban export trade, the impact indicators are different. According to the special geographical environment, congenital conditions, and preferential policy environment of the urban export trade system, this paper divides the macroeconomic factors that affect urban export trade into five major factors: national macroeconomic factors, regional economic factors, regional natural conditions, policy factors, and international economic factors. The national macroeconomic factors mainly include the national economic growth of the country and other regions, the export trade volume of the country and other regions, the consumption index and household savings of the country and other regions, and the industrial production index of the country and other regions. The economic factors of urban areas mainly include influencing indicators such as national economic growth in urban areas, regional industrial production index and output value, consumption index, household savings, and foreign capital utilization. The natural conditions of urban areas mainly include influencing indicators such as the comprehensive value of the surrounding environment and economy of the city, the throughput of regional ports, the comprehensive value of highway and railway routes, the concentration of natural resources, and labor costs. Policy factors mainly include influencing indicators such as export tariffs, exchange rates, and import and export incentive policies. In this paper, the above four types of indicators are collectively referred to as domestic macro factors. International economic factors mainly include international economic growth, total international imports and exports, comprehensive industrial index of various countries, labor costs of various countries, bilateral trade relations between countries and my country, and nontariff trade barriers.


## 4.2. Selection of Experimental Data

According to the established indicator system, the data collected on the export value and impact indicators of urban mechanical and electrical products are shown in Figure3. The data sources are the State Administration of Taxation, Customs, Statistical Yearbook, and City Statistics Yearbook. The export encouragement policy is a vague factor. We use the expert scoring method to determine its degree of membership.(6)ai=0.85,great effect,0.35,obvious effect,0.15,small effect.Figure 3
Raw data of impact indicators.


## 4.3. Data Preprocessing

The data preprocessing steps are as follows: the purpose of data inspection is to test the correctness, completeness, and consistency of statistics. The completeness and correctness of the data are essential. In addition, the consistency of the statistical caliber is also very important. If the data of some samples are taken from other calibers, it will have an impact on the relationship between the analysis indicators. Due to the different dimensions and value ranges of the data, sometimes the values of different indicators are very different. When solving problems, often large indicators have a large impact on the results, while factors with smaller values and higher importance may play a role. The effect is small. In order to avoid this situation, it is often necessary to normalize the data to change all the parameters to the same range. In data mining technology, data standardization is an important part of data preprocessing, which is a type of data transformation, which is used to transform data into a form suitable for mining processing.Data standardization, also called data standardization, refers to scaling attribute data to a small specific interval. For classification algorithms involving distance measurement, such as support vector machines and neural network algorithms, the standardization of data is particularly useful. The standardization of input values for the attribute measurement of training samples will help speed up machine learning. Standardization of the distance-based method can prevent the criterion with a larger initial domain from being overweighted compared to the criterion with a smaller initial domain, which will affect the prediction effect. Before the fuzzy neural network learning and training, the input and output raw data should be standardized preprocessing.Data preprocessing generally has different calculation formulas according to the difference in the correlation between input and output variables and the purpose of preprocessing. In this paper, a linear function is used to perform standardized preprocessing on the original data. The result of the preprocessing maps the original data to the interval range of [0, l]. Generally, a better prediction effect can be obtained when the standardized sequence is distributed in the interval of [0.1, 0.9]. Therefore, in order to normalize the original data to this interval as much as possible, this paper analyzes the minimum and the maximum scaled appropriately; the standardized data sample set is shown in Figure4.Figure 4
Standardized data sample set.


## 4.4. Correlation Analysis

The data analysis tools used are used to analyze the correlation between the respective indicators and the export trade volume, as shown in Figure5.Figure 5
Correlation between export value and influencing factors.The threshold for screening indicators based on the correlation coefficient is set to 0.5, and those factor variables that have a correlation coefficient less than 0.5 with the target variable are eliminated. At this time, the remaining input indicators are the actual use of foreign capital, the year-end balance of RMB savings deposits of urban and rural residents, the total investment in fixed assets, and the GDP. 10 indicators include the contribution of industrial industry to the growth of regional GDP, scientific expenditure, the purchase price index of raw materials, fuel and power, the exchange rate of US dollar to RMB, and the general consumer price index. As Figure5 reflects only a simple correlation between the dependent variable and the independent variable, the width of the threshold is set to 0.1 to allow more indicators that may hide important information to enter the model and the final indicator system. The 10 indicators of the country are used as input for foreign trade export forecasts.


## 4.5. Fuzzy Neural Network Forecasting Model for Forecasting Foreign Trade Exports

According to the calculation steps of the hybrid fuzzy neural network prediction model proposed in this article, the export volume of urban electromechanical products is predicted. The training parameters of the network are determined through multiple trials, and we choose the number of fuzzy rules to be 5. There are 10 input nodes in the network, which represent various influencing factors and normalize the input data according to formula (2). There is one output node of the network, and the prediction result of the model is calculated inversely according to formula (2) to obtain formula (3), and the prediction result of the same measurement standard as the original data is obtained.The prediction result of the fuzzy neural network model shows that the training sample has a high prediction accuracy under the control of the square error, which is basically within the range of 1%. The training sample prediction result has high accuracy and the error is within 5%, indicating that the export trade volume is in the model. The following has a good forecasting effect. The forecasted data basically fits the actual data of the export trade volume shown in Table1, indicating that the fuzzy neural network forecasting model for the export trade volume is reasonable and usable. The error distribution is shown in Figure 6.Table 1
Forecast value and error of export of mechanical and electrical products.
Actual valueEstimated valueStandard error201533.4133.02−0.015201645.2346.310.002201757.3256.830.034201858.4260.250.042201962.4364.150.025Figure 6
Forecast value and error of export of mechanical and electrical products.It can be seen from Figure6 that the fuzzy neural network model has a better fitting effect for urban export forecasts, with an average error of less than 1%; in particular, the forecast error from 2015 to 2019 is lower than 0.8%, which is relatively large. For example, the Ministry of Foreign Trade and Economic Cooperation implemented the export contract management responsibility system for its specialized foreign trade corporations, and the municipality was directly under the jurisdiction of the central government. However, the limited selection of data samples made foreign trade exports. The changes brought about by policy changes were not fully manifested, and a certain delay occurred. Nevertheless, it precisely reflects the influence of ambiguity factors (changes in export incentive policies) on modeling.


## 4.6. Analysis and Comparison of Forecast Results

As shown in Figure7, we further handle the relationship between quantitative forecasting and decision analysis and establish a feedback mechanism. The quantitative analysis results of the model should serve for decision-making support. Therefore, on the basis of the predicted results, we can further discuss how to formulate reasonable policies and measures to enhance the ability of enterprises to compete internationally for export products and promote the development of export trade. This article uses the correlation analysis of the data growth rate to screen the indicators, and the results are good, but it can be combined with other index reduction methods (such as rough sets) for in-depth research to further optimize the structure of the model. As far as the fuzzy neural network is concerned, the key to constructing the model is the acquisition of fuzzy rules. There is currently no better solution to the redundancy problem of fuzzy rules. Therefore, the algorithm for generating fuzzy rules can be further studied.Figure 7
Comparison of model predictions.It can be seen from the comparative analysis of the results obtained in Figure8 that the prediction results obtained by using fuzzy neural network for prediction analysis fit the actual situation better; MSE and MAPE have a greater degree of reduction compared with other prediction methods, indicating that the method is an effective and feasible method for foreign trade forecasting.Figure 8
Comparison of prediction effects of various prediction methods.


## 5. Conclusion

This paper introduces the fuzzy neural network method and uses its advantages in solving fuzzy, nonlinear, and high-dimensional problems to model export trade forecasts. The main work and conclusions of this paper are summarized as follows: a systematic review and elaboration of the results obtained by domestic and foreign scholars in the study of export trade forecasting methods and models, classification and induction of various forecasting models from the perspective of methodology, analysis of their respective advantages and disadvantages, and making a detailed summary and evaluation on the current research status of export trade forecasting. Based on the introduction of fuzzy logic theory and neural network, a prediction model based on fuzzy neural network is established, and the solution algorithm of the model and the general process of applying the fuzzy neural network model for prediction are given. We take the export of urban mechanical and electrical products and relevant statistical data of the market as an example and apply the fuzzy neural network forecast model to conduct empirical research. According to the characteristics of factors affecting export trade volume, an export trade evaluation index system is established in accordance with foreign trade theory. The original data sample set is selected, the indicators are screened through correlation analysis, and the data is preprocessed according to the standardized method of normalization of indicator data. In order to compare the forecasting effect of fuzzy neural network method and other forecasting methods on export trade, this paper selects the forecasting methods that are currently widely used in the field of foreign trade forecasting, such as ARIMA, regression analysis, gray system, support vector machine, neural network, and other models. Under the same training sample set and test sample set, model optimization and prediction are performed. The comparison results show that although the prediction accuracy of both is high, the prediction accuracy of the fuzzy neural network is higher and the promotion performance is better. In summary, the results of empirical research on urban foreign trade exports show that the application of the fuzzy neural network method to forecast foreign trade exports can achieve good forecasting results. The comparison results show that the fuzzy neural network forecasting model can be more in line with the characteristics and trends of export trade development.



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*Source: 5523222-2021-06-11.xml*