A polynomial fuzzy neural network for identification and control

This paper introduces a new neuro-fuzzy system, an effective optimization method through a genetic algorithm, a performance criterion for model selection, and a numerical example to illustrate the proposed modeling and control approach. The neuro-fuzzy system is based on the polynomial fuzzy neural network architecture. A new performance criterion is defined based on the Group Method of Data Handling; it minimizes the output error while preventing overfitting of the empirical data set. The neuro-fuzzy model is employed to provide optimum set points for low-level control activity.

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