Training fuzzy systems with the extended Kalman filter

Training fuzzy systems with the extended Kalman filter

7 The generation of membership functions for fuzzy systems is a challenging problem. We show that for Mamdani-type fuzzy systems with correlation-product inference, centroid defuzzi cation, and triangular membership functions, optimizing 9 the membership functions can be viewed as an identi cation problem for a nonlinear dynamic system. This identi cation problem can be solved with an extended ...

Training fuzzy systems with the extended Kalman %lter

The generation of membership functions for fuzzy systems is a challenging problem. We show that for Mamdani-type fuzzy systems with correlation-product inference, centroid defuzzi%cation, and triangular membership functions, optimizing the membership functions can be viewed as an identi%cation problem for a nonlinear dynamic system. This identi%cation problem can be solved with an extended Kalm...

Fuzzy Adaptive Extended Kalman Filter

Kalman filtering is a method for estimating state variables of a dynamic systems recursively from noise-contaminated measurements. For systems with nonlinear dynamics, a natural extension of the Linear Kalman Filter (LKF), called Extended Kalman filter (EKF) is used. The Kalman filter represents one of the most popular estimation techniques for integrating signals from navigation systems, like ...

Extended Kalman Filter Based Fuzzy Adaptive Filter

Fuzzy Optimization Using Extended Kalman Filter

Abstract : Fuzzy Logic is based on the idea that in fuzzy sets each element in the set can assume a value from 0 to 1, not only 0 or 1, as in crisp set theory. The degree of membership function is defined as the gradation in the extent to which an element is belonging to the relevant sets. Optimizing the membership functions of a fuzzy system can be viewed as a system identification problem for...