A theoretical and empirical study of functional link neural networks (FLNNs) for classification

Satchidananda Dehuri, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

In this chapter, the primary focus is on theoretical and empirical study of functional link neural networks (FLNNs) for classification. We present a hybrid Chebyshev functional link neural network (cFLNN) without hidden layer with evolvable particle swarm optimization (ePSO) for classification. The resulted classifier is then used for assigning proper class label to an unknown sample. The hybrid cFLNN is a type of feed-forward neural networks, which have the ability to transform the non-linear input space into higher dimensional space where linear separability is possible. In particular, the proposed hybrid cFLNN combines the best attribute of evolvable particle swarm optimization (ePSO), back-propagation learning (BP-Learning), and Chebyshev functional link neural networks (CFLNN). We have shown its effectiveness of classifying the unknown pattern using the datasets obtained from UCI repository. The computational results are then compared with other higher order neural networks (HONNs) like functional link neural network with a generic basis functions, Pi-Sigma neural network (PSNN), radial basis function neural network (RBFNN), and ridge polynomial neural network (RPNN).

Original languageEnglish
Title of host publicationArtificial Higher Order Neural Networks for Computer Science and Engineering
Subtitle of host publicationTrends for Emerging Applications
PublisherIGI Global
Pages545-573
Number of pages29
ISBN (Print)9781615207114
DOIs
Publication statusPublished - 2010

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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    Dehuri, S., & Cho, S. B. (2010). A theoretical and empirical study of functional link neural networks (FLNNs) for classification. In Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications (pp. 545-573). IGI Global. https://doi.org/10.4018/978-1-61520-711-4.ch022