Abstract
With the gradual deepening of China’s reform and opening up, the degree of foreign development has been deepened, and its dependence on foreign trade has increased. The “export‐oriented” economic development has achieved results. Export trade is introducing advanced technology and equipment, expanding employment opportunities, and increasing government revenue. The export trade is affected by various domestic and international factors and is a complex nonlinear system. Although the traditional linear prediction method has the advantages of intuitiveness, simplicity, and strong interpretability, it is difficult to deal with the prediction problem of dynamic and complex nonlinear systems. The neural network is a nonlinear dynamic system, with strong nonlinear mapping ability, strong robustness, and fault tolerance. It has unique advanced advantages for solving nonlinear problems and is very suitable for solving nonlinear problems.
Highlights
With the advancement of science and technology, human material civilization has developed to an unprecedented height, and the world economy is moving towards internationalization, globalization, international trade, and international division of labor
Cooperation is the trend of world economic development, and the economic development and technological progress of a country is increasingly influenced and dependent on other countries
International trade encourages products, technologies, and resources of all countries to enter international exchanges, complementing each other. e exchange of international commodities has promoted the division of labor and cooperation among countries and promoted the process of global economic integration
Summary
Since World War II, the international situation has changed a lot. Peace and development have become the theme of the world. E evaluation model of foreign trade development status constructed by BP neural network can reduce the influence of uncertain, multifactor, nonlinear, and other factors in the evaluation index, and can describe the complex nonlinear relationship existing in the evaluation index data, thereby improving the evaluation accuracy. (2) Use the improved particle swarm algorithm to optimize the construction of the BP neural network model, the process is as follows. Where z is the k-th generation position vector of the particle that needs speed adjustment; c is the learning factor; r is a random number between [0, 1]; Inertia weight w is used to adjust the influence degree of the particle’s previous velocity on the current velocity is derived from the quadratic function fitting, and the following weight adjustment formula is proposed: Neural network topology
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