Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7786
Title: VSC perspective for neurocontroller tuning
Authors: Efe, Mehmet Önder
Keywords: Gaussian radial basis function networks
sliding mode control
Issue Date: 2006
Publisher: Springer-Verlag Berlin
Source: 16th International Conference on Artificial Neural Networks (ICANN 2006) -- SEP 10-14, 2006 -- Athens, GREECE
Series/Report no.: Lecture Notes in Computer Science
Abstract: Compact representation of knowledge having strong internal interactions has become possible with the developments in neurocomputing and neural information processing. The field of neural networks has offered various solutions for complex problems, however, the problems associated with the learning performance has constituted a major drawback in terms of the realization performance and computational requirements. This paper discusses the use of variable structure systems theory in learning process. The objective is to incorporate the robustness of the approach into the training dynamics, and to ensure the stability in the adjustable parameter space. The results discussed demonstrate the fulfillment of the design specifications and display how the strength of a robust control scheme could be an integral part of a learning system. This paper discusses how Gaussian radial basis function neural networks could be utilized to drive a mechatronic system's behavior into a predefined sliding regime, and it is seen that the results are promising.
URI: https://hdl.handle.net/20.500.11851/7786
ISBN: 3-540-38625-4
ISSN: 0302-9743
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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