Performance Simulation of Identification System Based on Improved Neural Network Algorithm

Abstract

After decades of development, neural network theory has made considerable progress in many research fields such as pattern recognition, automatic control, signal processing, decision support, and artificial intelligence. This article discusses the application of neural networks in pattern recognition and system recognition and proposes several new methods for recognizing system models and recognizing model parameters. In order to achieve high-precision control of smart structure actuators, a robust model must first be created. For various modeling tasks, many scientists have done a good job and proposed different modeling methods. There are three main ways to create a system model: one is a physical model based on the mechanism of the material itself, the other is an operator model based on experimental phenomena, and the third is an intelligent model based on computer intelligence. The problem of the recognition system stems from the fact that with the development of science and technology, the research methods of various disciplines have been further quantified. In industrial practice and scientific experiments, the purpose of observing and calculating the quantitative identification of complex objects that need to be studied is usual. According to its inherent laws, it is necessary to establish a mathematical model of the research object in order to make decisions such as analysis, design, prediction, and control. This article uses the neural network model to study the best improvement method of the dynamic process. The research results of this article represent the theoretical basis of future scientific research to a certain extent and have important research value.