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Evolving Connection Weights for Pattern Storage and Recall in Hopfield Model of Feedback Neural Networks Using a Genetic Algorithm

ISSN: 2229 - 6735
Undergraduate Theses from gdlhub / 2017-08-14 11:52:34
Oleh : T. P. Singh and Suraiya Jabin, International Journal on Soft Computing
Dibuat : 2012-07-03, dengan 1 file

Keyword : Hopfield Neural Network, genetic algorithm, associative memory, weight matrices, population generation technique, fitness function
Subjek : Evolving Connection Weights for Pattern Storage and Recall in Hopfield Model of Feedback Neural Networks Using a Genetic Algorithm
Url : http://airccse.org/journal/ijsc/papers/3211ijsc05.pdf
Sumber pengambilan dokumen : Internet

In this paper, implementation of a genetic algorithm has been described to store and later, recall of some


prototype patterns in Hopfield neural network associative memory. Various operators of genetic algorithm


(mutation, cross-over, elitism etc) are used to evolve the population of optimal weight matrices for the


purpose of storing the patterns and then recalling of the patterns with induced noise was made, again using


a genetic algorithm. The optimal weight matrices obtained during the training are used as seed for starting


the GA in recalling, instead starting with random weight matrix. A detailed study of the comparison of


results thus obtained with the earlier results has been done. It has been observed that for Hopfield neural


networks, recall of patterns is more successful if evolution of weight matrices is applied for training


purpose also.

Deskripsi Alternatif :

In this paper, implementation of a genetic algorithm has been described to store and later, recall of some


prototype patterns in Hopfield neural network associative memory. Various operators of genetic algorithm


(mutation, cross-over, elitism etc) are used to evolve the population of optimal weight matrices for the


purpose of storing the patterns and then recalling of the patterns with induced noise was made, again using


a genetic algorithm. The optimal weight matrices obtained during the training are used as seed for starting


the GA in recalling, instead starting with random weight matrix. A detailed study of the comparison of


results thus obtained with the earlier results has been done. It has been observed that for Hopfield neural


networks, recall of patterns is more successful if evolution of weight matrices is applied for training


purpose also.

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