FUZZY PNN ALGORITHM AND ITSAPPLICATION TO NONLINEARPROCESSES
Tae-Chon Ahn, Sang-Moon Ryu · International Journal of General Systems · 2001
In this paper, a fuzzy Polynomial Neural Network (PNN) algorithm is proposed to estimate the structure and parameters of fuzzy model, using the PNN based on Group Method of Data Handling (GMDH) algorithm. The new algorithm uses PNN algorithm and fuzzy reasoning in order to identify the premise structure 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 NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy PNN. The simulation results show that the proposed technique can produce the fuzzy model with higher accuracy and feasibility than other works achieved previously. This algorithm will be applied to limited data processes with several inputs.