Research on Fuzzy Enhanced Learning Model of Multienhanced Signal Learning Automata

Abstract

With the continuous iteration of information technology, information presents an explosive development trend, so how to effectively classify, retrieve, and summarize information is very important. Data classification and mining technology as the key technology to solve the aforementioned problems is of great significance. At present, in the fields of data processing, information classification and machine learning reinforcement learning, it is usually based on the learning automata, but the traditional learning automata algorithm cannot meet the development of information technology in terms of convergence and learning rate. In order to improve the convergence stability and learning efficiency of the learning automata algorithm in random environment and enhance its learning ability in dealing with large-scale space and action, this paper innovatively proposes an adaptive fuzzy reinforcement learning model of the learning automata, which makes full use of the randomness of gradient information to solve its stability problem, and abandons the original reinforcement learning method. For the linear function, an adaptive fuzzy numerical superposition algorithm is used to further enhance its learning rate and improve its stability. The experimental results show that the proposed learning automata fuzzy reinforcement learning model has practical application value, and can solve the problems of unstable convergence and poor learning efficiency of the current learning automata.