Pattern Analysis and Applications

, Volume 1, Issue 1, pp 52–61

Resolution of pattern recognition problems using a hybrid Genetic/Random Neural Network learning algorithm

  • J. Aguilar
  • A. Colmenares

DOI: 10.1007/BF01238026

Cite this article as:
Aguilar, J. & Colmenares, A. Pattern Analysis & Applic (1998) 1: 52. doi:10.1007/BF01238026


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.


Associative memoryEvolutionary learningGenetic algorithmGradient descent rulePattern recognitionRandom Neural Network

Copyright information

© Springer-Verlag London Limited 1998

Authors and Affiliations

  • J. Aguilar
    • 1
  • A. Colmenares
    • 1
  1. 1.CEMISID, Dpto. de Computación, Facultad de IngenieríaUniversidad de los AndesMéridaVenezuela