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

Satchidananda Dehuri, Sung Bae Cho

Research output: Contribution to journalArticlepeer-review

112 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)187-205
Number of pages19
JournalNeural Computing and Applications
Volume19
Issue number2
DOIs
Publication statusPublished - 2010

Bibliographical note

Funding Information:
Authors would like to thank BK21 research program on Next Generation Mobile Software at Yonsei University, South Korea for their financial support. The authors greatly appreciate all the reviewers’ constructive comments that motivated them to think more and improve the presentation of this paper.

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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