On estimation of temporal fuzzy sets for signal analysis: FCM vs. FMLE approaches

Abstract

Estimation of temporal fuzzy sets that model dynamic processes is discussed. It has been found that although poles of attraction can be estimated fairly well with different fuzzy partitioning algorithms, membership function estimates may fail in accurately describing dynamic changes within the observed signals. Two types of fuzzy partitioning algorithms are compared: fuzzy c-means (FCM) and fuzzy maximum likelihood (FMLE). The simulations performed on quasi stationary Gaussian signals suggest that the membership functions estimated by FMLE fail to follow continuous changes of dynamics, while those estimated by FCM provide a good compromise between precision and physical relevance.