Application of fuzzy sets theory in process data filtering

Fuzzy sets theory is introduced and applied to the problems of signal restoration. A fuzzy approach for system identification and state estimation is developed. The estimated values obtained from the fuzzy model are in the form of fuzzy numbers which present the possibility of the system structure. Both L-R and symmetric triangular type membership functions are employed to derive the fuzzy-data based estimator (FBE). Illustrative examples with white and color noise are provided to demonstrate the applicability and effectiveness of the developed FBE. Comparisons between the FBE and various modified Kalman filter are also included.