Feedback analysis of radial basis functions neural networks via small gain theorem

S. Saad Azhar Ali; Muhammad Shafiq; Jamil M. Ba-Khashwain; Fouad M. Al-Sunni

doi:10.3182/20080706-5-KR-1001.1686

Feedback analysis of radial basis functions neural networks via small gain theorem

S. Saad Azhar Ali, Muhammad Shafiq, Jamil M. Ba-Khashwain, Fouad M. Al-Sunni

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

Abstract

Radial basis function neural networks are used in a variety of applications such as pattern recognition, nonlinear identification, control, time series prediction, etc. In this paper, feedback analysis of the learning algorithm of radial basis function neural networks is presented. It studies the robustness of the learning algorithm in the presence of uncertainties that might be due to noisy perturbations at the input or to modeling mismatch. The learning scheme is first associated with a feedback structure and then the stability of that feedback structure is analyzed via small gain theorem. The analysis suggests bounds on the learning rate in order to guarantee that the learning algorithm will behave as robust nonlinear filters and optimal choices for faster convergence speeds.

Original language	English
Title of host publication	Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
Edition	1 PART 1
DOIs	https://doi.org/10.3182/20080706-5-KR-1001.1686
State	Published - 2008

Publication series

Name	IFAC Proceedings Volumes (IFAC-PapersOnline)
Number	1 PART 1
Volume	17
ISSN (Print)	1474-6670

Bibliographical note

Funding Information:
★ This work is sponsored by King Fahd University of Petroleum & Minerals and SABIC under project SABIC 2006-11

Funding Information:
The authors acknowledge the support of King Fahd University of Petroleum & Minerals and SABIC for funding this work under project SABIC 2006-11.

Keywords

Closed loop identification
Identification for control
Nonlinear system identification

ASJC Scopus subject areas

Control and Systems Engineering

Access to Document

10.3182/20080706-5-KR-1001.1686

Cite this

Ali, S. S. A., Shafiq, M., Ba-Khashwain, J. M., & Al-Sunni, F. M. (2008). Feedback analysis of radial basis functions neural networks via small gain theorem. In Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC (1 PART 1 ed.). (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 17, No. 1 PART 1). https://doi.org/10.3182/20080706-5-KR-1001.1686

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note = "Funding Information: ★ This work is sponsored by King Fahd University of Petroleum & Minerals and SABIC under project SABIC 2006-11 Funding Information: The authors acknowledge the support of King Fahd University of Petroleum & Minerals and SABIC for funding this work under project SABIC 2006-11.",

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doi = "10.3182/20080706-5-KR-1001.1686",

language = "English",

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Feedback analysis of radial basis functions neural networks via small gain theorem. / Ali, S. Saad Azhar; Shafiq, Muhammad; Ba-Khashwain, Jamil M. et al.
Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC. 1 PART 1. ed. 2008. (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 17, No. 1 PART 1).

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

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Feedback analysis of radial basis functions neural networks via small gain theorem

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

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Cite this