Fuzzy PNN algorithm and its application to nonlinear processes

In this paper, a fuzzy Polynomial Neural Network (PNN) algorithm is propose d to estimate the structure and parameters of fuzzy model, using the PNN ba sed on Group Method of Data Handling (GMDH) algorithm. The new algorithm us es PNN algorithm and fuzzy reasoning in order to identify the premise struc ture and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NO, emission process of gas turbi ne power plants are used for the purpose of evaluating the performance of t he fuzzy PNN. The simulation results show that the proposed technique can p roduce the fuzzy model with higher accuracy and feasibility than other work s achieved previously. This algorithm will be applied to limited data proce sses with several inputs.