Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99595
Title: A fully complex-valued radial basis function classifier for real-valued classification problems
Authors: Suresh, Sundaram
Sundararajan, Narasimhan
Savitha, R.
Kim, H. J.
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2011
Source: Savitha, R., Suresh, S., Sundararajan, N., & Kim, H. J. (2011). A fully complex-valued radial basis function classifier for real-valued classification problems. Neurocomputing, 78(1), 104-110.
Series/Report no.: Neurocomputing
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.
URI: https://hdl.handle.net/10356/99595
http://hdl.handle.net/10220/13650
DOI: http://dx.doi.org/10.1016/j.neucom.2011.05.036
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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