Abstract
According to the BP neural network algorithm, for the problem that the input variables of the BP network cannot be automatically optimized during the modeling process of the multivariable complex system, it is used to establish a neural network optimization algorithm according to the gray correlation analysis (GM2 BPANN). Using the data of China’s grain production forecast, the stepwise regression method and the gray GM (1, N) model method were compared and tested. The results show that the new model can comprehensively and extensively process a large number of input variables by using the concept of the gray correlation degree, without having to go through special subjective screening and hence improving the adaptability of the BP network, and at the same time, it can obtain better prediction accuracy and stability. Through the empirical test, the prediction ability of the three methods of regression, GM (1, N) gray system, and GM‐BPANN model is compared. It is proved that the GM‐BPANN optimization algorithm that combines the gray relational analysis and BP neural network method can enhance the multivariable processing ability and network adaptability of the BP network and has good prediction accuracy and stability.
Highlights
E artificial neural network is a mathematical method that does not need to determine the mutual mapping relationship between input and output in advance
Deep learning lacks the ability of humans to learn new things quickly. erefore, with the aim of solving the problem of deep learning model’s serious dependence on the amount of data, people gradually began to focus on small sample learning technology
In view of the inherent shortcomings of the back propagation (BP) network that cannot automatically optimize multiple variables, the learning process converges slowly and is easy to fall into local minima; many scholars at home and abroad use rough sets and genetic algorithms in combination with them to achieve the purpose of optimization. e improvement of it is continuous
Summary
E results show that the new model can comprehensively and extensively process a large number of input variables by using the concept of the gray correlation degree, without having to go through special subjective screening and improving the adaptability of the BP network, and at the same time, it can obtain better prediction accuracy and stability. In this study, based on the BP neural network algorithm, try to combine it with the gray system correlation analysis method to solve the optimization selection problem of the BP neural network input variables and establish the GM 2 BPANN optimization model. Rough the calculation of the gray correlation degree, a large number of input variables can be processed comprehensively and widely without special subjective screening, enhancing the adaptability of the BP network, and at the same time, it can obtain better prediction accuracy and stability.
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