Open AccessA Nonlinear System Identification Method Based on Adaptive Neural Network
Author(s) -
Junzi Sun,
Liyun Liyun
Publication year - 2021
Publication title -
cit. journal of computing and information technology/journal of computing and information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.169
H-Index - 27
eISSN - 1846-3908
pISSN - 1330-1136
DOI - 10.20532/cit.2020.1005179
Subject(s) - computer science , particle swarm optimization , artificial neural network , nonlinear system , identification (biology) , identifier , convergence (economics) , system identification , artificial intelligence , machine learning , data mining , physics , botany , quantum mechanics , measure (data warehouse) , economics , biology , programming language , economic growth
Nonlinear system identification (NSI) is of great significance to modern scientific engineering and control engineering. Despite their identification ability, the existing analysis methods for nonlinear systems have several limitations. The neural network (NN) can overcome some of these limitations in NSI, but fail to achieve desirable accuracy or training speed. This paper puts forward an NSI method based on adaptive NN, with the aim to further improve the convergence speed and accuracy of NN-based NSI. Specifically, a generic model-based nonlinear system identifier was constructed, which integrates the error feedback and correction of predictive control with the generic model theory. Next, the radial basis function (RBF) NN was optimized by adaptive particle swarm optimization (PSO), and used to build an NSI model. The effectiveness and speed of our model were verified through experiments. The research results provide a reference for applying the adaptive PSO-optimized RBFNN in other fields.