Evolutionary Neural Networks for Nonlinear Dynamics Modeling

In this paper the evolutionary design of a neural network model for predicting nonlinear systems behavior is discussed. In particular, the Breeder Genetic Algorithms are considered to provide the optimal set of synaptic weights of the network. The feasibility of the neural model proposed is demonstrated by predicting the Mackey-Glass time series. A comparison with Genetic Algorithms and Back Propagation learning technique is performed.

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