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

Authors:
Satchidananda Dehuri;Sung-Bae Cho
Affiliations:
Fakir Mohan University, Department of Information and Communication Technology, Vyasa Vihar, 756019, Balasore, Orissa, India;Yonsei University, Soft Computing Laboratory, Department of Computer Science, 262 Seongsanno, Seodaemun-gu, 120-749, Seoul, Korea
Venue:
Neural Computing and Applications - Special Issue - KES2008
Year:
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.