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Summary: Published in the Proceedings of the International Conference on Artificial Neural
Networks (ICANN-2007), Lecture Notes in Computer Science (J. Marques de Sá et al.
-Eds.), vol. 4668, Part I, Berlin, Heidelberg, New York, 2007, pp. 874-883.
874
Solving Selected Classification Problems in
Bioinformatics Using Multilayer Neural Network based
on Multi-Valued Neurons (MLMVN)
Igor Aizenberg1
and Jacek M. Zurada2
1- Texas A&M University-Texarkana; 2- University of Louisville
igor.aizenberg@tamut.edu (IA)
jacek.zurada@louisville.edu (jmz)
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 (complex-
valued 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
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