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Identification of Dynamical Systems Using Recurrent Neural Networks with Structure Optimization Utilizing Genetic Algorithms | ||
| Engineering and Technology Journal | ||
| Volume 24, Issue 2, February 2005, Pages 129-139 PDF (1.81 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.30684/etj.24.2A.7 | ||
| Author | ||
| Ahmed I. Al - Dulaimy | ||
| 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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