Resolution of pattern recognition problems using a hybrid Genetic/Random Neural Network learning algorithm
 J. Aguilar,
 A. Colmenares
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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 rulebased 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.
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 Title
 Resolution of pattern recognition problems using a hybrid Genetic/Random Neural Network learning algorithm
 Journal

Pattern Analysis and Applications
Volume 1, Issue 1 , pp 5261
 Cover Date
 19980301
 DOI
 10.1007/BF01238026
 Print ISSN
 14337541
 Online ISSN
 1433755X
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 Associative memory
 Evolutionary learning
 Genetic algorithm
 Gradient descent rule
 Pattern recognition
 Random Neural Network
 Industry Sectors
 Authors

 J. Aguilar ^{(1)}
 A. Colmenares ^{(1)}
 Author Affiliations

 1. CEMISID, Dpto. de Computación, Facultad de Ingeniería, Universidad de los Andes, Av. Tulio Febres, Mérida, Venezuela