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Neural Computing and Applications

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01.03.2010 | KES 2008

A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN

verfasst von: Satchidananda Dehuri, Sung-Bae Cho

Erschienen in: Neural Computing and Applications | Ausgabe 2/2010

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Abstract

Functional link neural network (FLNN) is a class of higher order neural networks (HONs) and have gained extensive popularity in recent years. FLNN have been successfully used in many applications such as system identification, channel equalization, short-term electric-load forecasting, and some of the tasks of data mining. The goals of this paper are to: (1) provide readers who are novice to this area with a basis of understanding FLNN and a comprehensive survey, while offering specialists an updated picture of the depth and breadth of the theory and applications; (2) present a new hybrid learning scheme for Chebyshev functional link neural network (CFLNN); and (3) suggest possible remedies and guidelines for practical applications in data mining. We then validate the proposed learning scheme for CFLNN in classification by an extensive simulation study. Comprehensive performance comparisons with a number of existing methods are presented.

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Titel: A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN
verfasst von: Satchidananda Dehuri
Sung-Bae Cho
Publikationsdatum: 01.03.2010
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 2/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0288-5

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