Using self-constructing recurrent fuzzy neural networks for identification of nonlinear dynamic systems

被引：5

作者：

Li, Qinghai ^{[1
]}

Lin, Ye ^{[1
]}

Lin, Rui-Chang ^{[2
]}

Meng, Hao-Fei ^{[3
]}

机构：

[1] Zhejiang Ind & Trade Vocat Coll, Dept Elect Engn, Wenzhou 325003, Peoples R China

[2] Guangzhou Panyu Polytech, Coll Mech & Elect Engn, Guangzhou 511483, Peoples R China

[3] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL | 2019年 / 33卷 / 04期

关键词：

self-constructing FNN; neural network; fuzzy system; nonlinear system; system identification; structure learning; parameter learning; recurrent path; gradient descent method; LEARNING ALGORITHM; FUNCTION APPROXIMATION; MODEL;

D O I：

10.1504/IJMIC.2019.107461

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, the self-constructing recurrent fuzzy neural network (SCRFNN) is applied for nonlinear dynamical system identification (NDSI). The SCRFNN is a novel fuzzy neural network (FNN) by adding a recurrent path in each node of the hidden layer of self-constructing FNN, which contains two learning phases. Specifically, the structure learning is based on partition of the input space and the parameter learning is based on the supervised gradient descent method using a delta adaptation law. The SCRFNN can decrease the minimum firing strength in each learning cycle and the number of hidden neurons which is an FNN with high accuracy and compact structure compared with several other neural networks. The performance of SCRFNN in NDSI is further verified in simulation.

引用

页码：378 / 386

页数：9