The Optimization of Nonlinear Systems Identification Based on Genetic Algorithms
Xin Tan, Huaqian Yang · 2006
Gaussian-Hopfield neural networks (GHNNs) are widely used in identifying nonlinear systems, however, the delta-learning rule is easy to encounter the local minima problem. In this paper, genetic algorithms are adopted to overcome the problem. The proposed method is used to improve the speed of searching for a set of optimal parameters for the GHNNs. To verify the validity of the proposed method, simulation experiments are provided. The results have been shown that the ability of the proposed method to identify nonlinear systems is satisfactory