Design of a chaotic neural network for training and retrieval of grayscale and binary patterns

Authors:
A. Taherkhani;S. A. Seyyedsalehi;A. H. Jafari
Affiliations:
Department of Biomedical Engineering, Amirkabir University of Technology, Tehran 15875-4413, Iran;Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran;Medical Physics & Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Venue:
Neurocomputing
Year:
2011

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Abstract

Experimental and theoretical evidence shows that biological system processing behavior has nonlinear and chaotic properties. The ability of emerging various solutions for a problem and the existence of a supervisor to guide this variety to become close to the goal, are the two main properties of a problem solver. In this paper, a chaotic neural network which uses chaotic nodes with the logistic map as activation functions is designed to make the ability of emerging various solutions and an NDRAM is considered as a supervisor to guide these various solutions. The proposed chaotic neural network has better performance in comparison with Hopfield, NDRAM, and L. Zhao et al. ChNN.