Abstract
In this paper, a new control parameter adaptation scheme is introduced into the classical differential evolution (DE) algorithm. Then, a method for nonlinear system identification is proposed. The method combines modified differential evolution (MDE) and radial basis function (RBF) neural networks, which can auto-configure the structure of RBF networks and obtain the model parameters. The RBF network structure and parameters could be determined simultaneously based on input-output data without a priori knowledge. Finally, an example of nonlinear function identification is given to illustrate the effectiveness of the proposed approach.
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