A nonlinear system identification approach based on neurofuzzy networks

被引：0
|
作者：
Li, Y [1 ]
Zhao, XY [1 ]
Jiao, LC [1 ]
机构：
[1] Xidian Univ, Key Lab Signal Proc, Xian 710071, Peoples R China
来源：
2000 5TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS I-III | 2000年
关键词：
neurofuzzy networks; structure identification; parameter identification; system identification;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper presents a nonlinear system identification approach based on neurofuzzy networks. This method consists of two main step: STEP 1 is concerned with the structure identification or learning, which includes selection of input variables and determination of the number of fuzzy rules and the initial terms for membership functions. STEP 2 is concerned with parameter identification or learning. Its task is to adjust the weights of the neurofuzzy network, i.e., the antecedent and consequent parameters of rules, so that the error between the desired and real output is minimum. The effectiveness of the proposed technique is confirmed by simulation results.
引用
收藏
页码：1594 / 1597
页数：4
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