Solving selected classification problems in bioinformatics using multilayer neural network based on multi-valued neurons (MLMVN)

Authors:
Igor Aizenberg;Jacek M. Zurada
Affiliations:
Texas A&M University-Texarkana;University of Louisville
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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Abstract

A multilayer neural network based on multi-valued neurons (MLMVN) is a new powerful tool for solving classification, recognition and prediction problems. This network has a number of specific properties and advantages that follow from the nature of a multi-valued neuron (complexvalued weights and inputs/outputs lying on the unit circle). Its backpropagation learning algorithm is derivative-free. The learning process converges very quickly, and the learning rate for all neurons is self-adaptive. The functionality of the MLMVN is higher than the one of the traditional feedforward neural networks and a variety of kernel-based networks. Its higher flexibility and faster adaptation to the mapping implemented make it possible to solve complex classification problems using a simpler network. In this paper, we show that the MLMVN can be successfully used for solving two selected classification problems in bioinformatics.