A fully complex-valued radial basis function classifier for real-valued classification problems

R. Savitha, S. Suresh, N. Sundararajan, Hyong Joong Kim

Research output: Contribution to journalArticle

50 Citations (Scopus)

Abstract

In this paper, we investigate the decision making ability of a fully complex-valued radial basis function (FC-RBF) network in solving real-valued classification problems. The FC-RBF classifier is a single hidden layer fully complex-valued neural network with a nonlinear input layer, a nonlinear hidden layer, and a linear output layer. The neurons in the input layer of the classifier employ the phase encoded transformation to map the input features from the Real domain to the Complex domain. The neurons in the hidden layer employ a fully complex-valued Gaussian-like activation function of the type of hyperbolic secant (sech). The classification ability of the classifier is first studied analytically and it is shown that the decision boundaries of the FC-RBF classifier are orthogonal to each other. Then, the performance of the FC-RBF classifier is studied experimentally using a set of real-valued benchmark problems and also a real-world problem. The study clearly indicates the superior classification ability of the FC-RBF classifier.

Original languageEnglish
Pages (from-to)104-110
Number of pages7
JournalNeurocomputing
Volume78
Issue number1
DOIs
Publication statusPublished - 2012 Feb 15

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Keywords

  • Acoustic emission signal classification
  • Classification
  • Complex-valued classifiers
  • Fully complex-valued radial basis function
  • Orthogonal decision boundaries

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Cognitive Neuroscience

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