Resolution of pattern recognition problems using a hybrid Genetic/Random Neural Network learning algorithm Article Received: 29 September 1997 Revised: 07 February 1998 Accepted: 03 March 1998 DOI :
10.1007/BF01238026

Cite this article as: Aguilar, J. & Colmenares, A. Pattern Analysis & Applic (1998) 1: 52. doi:10.1007/BF01238026
Abstract Gelenbe has proposed a neural network, called a Random Neural Network, which calculates the probability of activation of the neurons in the network. In this paper, we propose to solve the patterns recognition problem using a hybrid Genetic/Random Neural Network learning algorithm. The hybrid algorithm trains the Random Neural Network by integrating a genetic algorithm with the gradient descent rule-based learning algorithm of the Random Neural Network. This hybrid learning algorithm optimises the Random Neural Network on the basis of its topology and its weights distribution. We apply the hybrid Genetic/Random Neural Network learning algorithm to two pattern recognition problems. The first one recognises or categorises alphabetic characters, and the second recognises geometric figures. We show that this model can efficiently work as associative memory. We can recognise pattern arbitrary images with this algorithm, but the processing time increases rapidly.

Keywords Associative memory Evolutionary learning Genetic algorithm Gradient descent rule Pattern recognition Random Neural Network

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© Springer-Verlag London Limited 1998

Authors and Affiliations 1. CEMISID, Dpto. de Computación, Facultad de Ingeniería Universidad de los Andes Mérida Venezuela