Document Type : Research Paper
Author
Abstract
Abstract :This paper presents a genetic learning for training recurrent neural networks ( RNNS ) using series - parallel modeling scheme . All weights of these networks are adjusted simultaneously with the optimization of RNNs structure . The evolutionary technique is based on genetic algorithms ( GAS ) with real coding operators used as a mean for training the RNNs of variable structure ( GAs are used for selecting an optimal number of hidden nodes for neuro - identifier , as well as training the network to minimize the error ) . The mean square error ( MSE ) function between the plant and the model output are optimized globally through generations of the genetic sea with elitism and hybrid selection method . Due to the mechanism of a hybrid selection method , elitism strategy and real - coding operators , the GA can find the best model for a given plant early from the first generations . The significance of the proposed identification approach is illustrated with simulated different examples for linear and non - linear plants off - line .
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