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.
Highlights
Export trade is a complex system with multiple factors, nonlinearities, and ambiguities. ere 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]
Relevant researchers have done a lot of research work on such a very difficult topic as a prediction. ese 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
Summary
Export trade is a complex system with multiple factors, nonlinearities, and ambiguities. ere 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]. 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. 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. Is article will construct an index system for export trade forecasting from different perspectives and apply the fuzzy neural network method to construct an export trade forecasting model. 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
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