Person Recognition Optimization Method Relying on Radial Basis Function Network

Abstract

Artificial NN is an intelligent model for information processing by simulating the organization and mechanism of the nervous system of the brain. Thanks to its strong self-learning ability, artificial NN can liberate people's labor force to a large extent, so it has been widely studied and alied. The theoretical study of artificial NN can effectively explain the alication principle and potential problems of NN, so that it can be effectively improved and developed. Therefore, the main purpose of this paper is to study the optimization method of person recognition (PR) based on RBF network. In this paper, the damping coefficients of RBF networks with uniform center distribution are studied. This stochastic learning method is an important sulement to how to determine the center and limiting factor of the RBF of the NN, and can also improve the training efficiency of the network. The main research results of this paper are developed by analyzing the convergence of stochastic RBFNN and constructing corresponding models and algorithms. Compared with other algorithms, the results show that the ARBFNN algorithm can make the RBFNN obtain a smaller training error, improve the generalization ability of the network, and have the ability to deal with large data sets and fast convergence. Recognition is more convenient and quicker.