Table of Contents
Advances in Artificial Neural Systems
Volume 2011, Article ID 107498, 10 pages
Research Article

A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore, Orissa 756019, India

Received 11 January 2011; Revised 17 April 2011; Accepted 28 May 2011

Academic Editor: Giacomo Indiveri

Copyright © 2011 Satchidananda Dehuri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A hybrid learning scheme (ePSO-BP) to train Chebyshev Functional Link Neural Network (CFLNN) for classification is presented. The proposed method is referred as hybrid CFLNN (HCFLNN). The HCFLNN is a type of feed-forward neural networks which have the ability to transform the nonlinear input space into higher dimensional-space where linear separability is possible. Moreover, the proposed HCFLNN combines the best attribute of particle swarm optimization (PSO), back propagation learning (BP learning), and functional link neural networks (FLNNs). The proposed method eliminates the need of hidden layer by expanding the input patterns using Chebyshev orthogonal polynomials. We have shown its effectiveness of classifying the unknown pattern using the publicly available datasets obtained from UCI repository. The computational results are then compared with functional link neural network (FLNN) with a generic basis functions, PSO-based FLNN, and EFLN. From the comparative study, we observed that the performance of the HCFLNN outperforms FLNN, PSO-based FLNN, and EFLN in terms of classification accuracy.