Solving Selected Classification Problems in Bioinformatics Using Multilayer Neural Network Based on Multi-Valued Neurons (MLMVN)

  • Igor Aizenberg
  • Jacek M. Zurada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4668)


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 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.


Hide Layer Unit Circle Hide Neuron Output Neuron Cellular Neural Network 
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© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Igor Aizenberg
    • 1
  • Jacek M. Zurada
    • 2
  1. 1.Texas A&M University-Texarkana 
  2. 2.University of Louisville 

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