An improved approach to nonlinear dynamical system identification using PID neural networks

被引：4

作者：

Li, SJ ^{[1
]}

Liu, YX ^{[1
]}

机构：

[1] Dalian Univ Technol, State Key Lab Struct Anal Ind Equipment, Dalian 116024, Peoples R China

来源：

INTERNATIONAL JOURNAL OF NONLINEAR SCIENCES AND NUMERICAL SIMULATION | 2006年 / 7卷 / 02期

关键词：

nonlinear dynamical system; system identification; neural network; discrete-time system;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The ability of a neural network to realize some complex nonlinear system makes it attractive for system identification. The problem of deterministic nonlinear system identification is considered as an application of PID neural network. PID neural network's weights are adjusted by the back-propagation algorithm, BFGS optimization algorithm and Levenberg-Marquardt approximation. Two examples are given to demonstrate the performance and efficiency of the proposed method. The simulation results show that the suggested method gives less effor minimization and faster convergence.

引用

页码：177 / 182

页数：6

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