Performance Analysis of Hopfield Model of Neural Network with Evolutionary Approach for Pattern Recalling
نویسنده
چکیده
ABSTRACT In the present paper, an effort has been made to compare and analyze the performance for pattern recalling with conventional hebbian learning rule and with evolutionary algorithm in Hopfield Model of feedback Neural Networks. A set of ten objects has been considered as the pattern set. In the Hopfield type of neural networks of associative memory, the weighted code of input patterns provides an auto-associative function in the network. The storing of the objects has been performed using Hebbian rule and recalling of these stored patterns on presentation of prototype input patterns has been made using both conventional hebbian rule and evolutionary algorithm. Exploration of the population generation techniques (mutation and elitism), crossover and setting up of proper fitness evaluation functions to generate the new population of the weight matrices from the optimal weight matrix of the stored patterns has been done. The simulated results show that the genetic algorithm is the best searching technique to recall the approximate input patterns.
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