Fuzzy-Logic Adapted for LMS Algorithm Based on q-Gradient

This paper studies the system identification problem using the q-least mean square (qLMS) algorithm. Particularly, the fuzzy-logic scheme is considered here. The proposed fuzzy-logic trained-qLMS (FLT-qLMS) algorithm is developed based on fuzzy-logic adapted, which averts the q value selection problem in practical applications. However, the performance of FLT-qLMS is subjected to the conflicted requirement of fixed step size. To completely devoid of parameter selection problem, a new fuzzy-logic scheme is further developed for online adjusting q value and step size simultaneously, resulting in enhanced FLT-qLMS (EFLT-qLMS) algorithm. Extensive simulations indicate the refined performance of the proposed algorithms in the context of system identification.