Approximation Capability of a Novel Neural Network Model for Dynamic Systems

被引：0

作者：

Zhang, Jianhai ^{[1
]}

Kong, Wanzeng ^{[1
]}

Zhang, Senlin ^{[2
]}

Liu, Meiqin ^{[2
]}

机构：

[1] Hangzhou Dianzi Univ, Coll Comp, Hangzhou, Zhejiang, Peoples R China

[2] Zhejiang Univ, Coll Elect Engn, Hangzhou, Zhejiang, Peoples R China

来源：

ICICTA: 2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL I, PROCEEDINGS | 2009年

基金：

中国国家自然科学基金;

关键词：

approximation capability; standard neural network model; recurrent neural network; dynamic systems; IDENTIFICATION;

D O I：

10.1109/ICICTA.2009.23

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The approximation power for dynamic systems of a novel neural network model-standard neural network model (SNNM) is examined. Applying Stone-Weierstrass theorem, it is proved that SNNM is capable of approximating dynamic systems to any degree of accuracy. Furthermore, the results are briefly extended for any bounded measurable functions. The approximation capability together with the learn ability justify the use of SNNM in practical applications.

引用

页码：59 / 62

页数：4

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