Multi-field data analysis method based on Bayesian information enhanced neural network

Abstract

A multi-field data analysis method based on a Bayesian information enhanced neural network comprises a BITRNN model, and a final optimization objective of the model combines expected cumulative rewards of reinforcement learning, an evidence lower bound (ELBO) of Bayesian reasoning and an exploration mechanism of information entropy. Through combination of Bayesian reasoning, information entropy and reinforcement learning, prediction and decision optimization in multi-field complex data are realized. The model provided by the invention can dynamically adapt to environmental changes and maintain efficient prediction performance in high-dimensional uncertain data. Through Bayesian reasoning, the model can effectively process parameter uncertainty; the introduction of the information entropy increases the exploratory performance of the strategy, and avoids falling into a local optimal solution. According to the method, theoretical advantages of Bayesian statistics, an information theory and reinforcement learning are fused, high-dimensional and high-uncertainty data can be effectively processed, the prediction precision and generalization ability of the model are improved, the method is suitable for various fields such as polymer material performance optimization, agricultural planting strategy making and financial investment decision making, and the method has wide application prospects. Wide application prospects and practical values are realized.

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