Approximation capability of a novel neural network model for dynamic systems

Jianhai Zhang; Wanzeng Kong; Senlin Zhang; Meiqin Liu

doi:10.1109/ICICTA.2009.23

Approximation capability of a novel neural network model for dynamic systems

Jianhai Zhang
, Wanzeng Kong
, Senlin Zhang
, Meiqin Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009
Pages	59-62
Number of pages	4
DOIs	https://doi.org/10.1109/ICICTA.2009.23
State	Published - 2009
Externally published	Yes
Event	2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009 - Changsha, Hunan, China Duration: 10 Oct 2009 → 11 Oct 2009

Publication series

Name	2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009
Volume	1

Conference

Conference	2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009
Country/Territory	China
City	Changsha, Hunan
Period	10/10/09 → 11/10/09

Keywords

Approximation capability
Dynamic systems
Recurrent neural network
Standard neural network model

Access to Document

10.1109/ICICTA.2009.23

Cite this

Zhang, J., Kong, W., Zhang, S., & Liu, M. (2009). Approximation capability of a novel neural network model for dynamic systems. In 2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009 (pp. 59-62). Article 5287709 (2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009; Vol. 1). https://doi.org/10.1109/ICICTA.2009.23

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title = "Approximation capability of a novel neural network model for dynamic systems",

abstract = "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.",

keywords = "Approximation capability, Dynamic systems, Recurrent neural network, Standard neural network model",

author = "Jianhai Zhang and Wanzeng Kong and Senlin Zhang and Meiqin Liu",

year = "2009",

doi = "10.1109/ICICTA.2009.23",

language = "英语",

isbn = "9780769538044",

series = "2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009",

pages = "59--62",

booktitle = "2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009",

note = "2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009 ; Conference date: 10-10-2009 Through 11-10-2009",

}

Zhang, J, Kong, W, Zhang, S & Liu, M 2009, Approximation capability of a novel neural network model for dynamic systems. in 2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009., 5287709, 2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009, vol. 1, pp. 59-62, 2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009, Changsha, Hunan, China, 10/10/09. https://doi.org/10.1109/ICICTA.2009.23

Approximation capability of a novel neural network model for dynamic systems. / Zhang, Jianhai; Kong, Wanzeng; Zhang, Senlin et al.
2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009. 2009. p. 59-62 5287709 (2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Kong, Wanzeng

AU - Zhang, Senlin

AU - Liu, Meiqin

PY - 2009

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N2 - 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.

AB - 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.

KW - Approximation capability

KW - Dynamic systems

KW - Recurrent neural network

KW - Standard neural network model

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DO - 10.1109/ICICTA.2009.23

M3 - 会议稿件

AN - SCOPUS:71949106769

SN - 9780769538044

T3 - 2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009

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BT - 2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009

T2 - 2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009

Y2 - 10 October 2009 through 11 October 2009

ER -

Approximation capability of a novel neural network model for dynamic systems

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Publication series

Conference

Keywords

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