IDENTIFICATION OF LINEAR AND NONLINEAR DYNAMIC-SYSTEMS USING RECURRENT NEURAL NETWORKS

被引：33

作者：

PHAM, DT ^{[1
]}

LIU, X ^{[1
]}

机构：

[1] UNIV WALES COLL CARDIFF,SCH ELECT ELECTR & SYST ENGN,INTELLIGENT SYST RES LAB,CARDIFF CF1 3YH,WALES

来源：

ARTIFICIAL INTELLIGENCE IN ENGINEERING | 1993年 / 8卷 / 01期

关键词：

RECURRENT NEURAL NETWORKS; ELMAN NETS; MODIFIED ELMAN NETS; DYNAMIC SYSTEM IDENTIFICATION;

D O I：

10.1016/0954-1810(93)90032-B

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes the use of Elman-type recurrent neural networks to identify dynamic systems. Networks as originally designed by Elman (Cognitive Sci., 1990, 14, 179-211) and also those in which self-connections are made to the context units were employed to identify a variety of linear and nonlinear systems. It was found that the latter networks were more versatile than the basic Elman nets in being able to model the dynamic behaviour of high order linear and nonlinear systems.

引用

页码：67 / 75

页数：9

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