IDENTIFICATION USING FEEDFORWARD NETWORKS

被引：39

作者：

LEVIN, AU ^{[1
]}

NARENDRA, KS ^{[1
]}

机构：

[1] YALE UNIV,DEPT ELECT ENGN,CTR SYST SCI,NEW HAVEN,CT 06520

来源：

NEURAL COMPUTATION | 1995年 / 7卷 / 02期

关键词：

D O I：

10.1162/neco.1995.7.2.349

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 is concerned with the identification of an unknown nonlinear dynamic system when only the inputs and outputs are accessible for measurement. Specifically we investigate the use of feedforward neural networks as models for the input-output behavior of such systems. Relying on the approximation capabilities of feedforward neural networks and under mild assumptions regarding the properties of the underlying nonlinear system, it is shown that there exists a feedforward network that for almost all inputs (an open and dense set) will display the input-output behavior of the system.

引用

页码：349 / 357

页数：9

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