A polynomial fuzzy neural network for identification and control
S. Kim, George Vachtsevanos · 2002
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.