Adaptive system identification using fuzzy inference based LMS algorithm

A fuzzy LMS algorithm is presented. The algorithm uses fuzzy inference to obtain the adaptive gain at every iteration of the LMS algorithm. The adaptive gain is obtained by a fuzzy system which has two inputs, an error signal and a one-step previous adaptive gain. This algorithm is applied to adapti...

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Bibliographic Details
Main Authors: Geun-Taek Ryu, Dong-Won Kim, Jung-Go Choe, Dae-Sung Kim, Hyeon-Deok Bae
Format: Conference Proceeding
Language:English
Subjects:
Adaptive algorithm
Adaptive filters
Adaptive systems
Convergence
Fuzzy sets
Fuzzy systems
Inference algorithms
Least squares approximation
System identification
Telecommunications
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Description
Summary:A fuzzy LMS algorithm is presented. The algorithm uses fuzzy inference to obtain the adaptive gain at every iteration of the LMS algorithm. The adaptive gain is obtained by a fuzzy system which has two inputs, an error signal and a one-step previous adaptive gain. This algorithm is applied to adaptive system identification of a hybrid system in a telecommunication network. In the experimental results, the proposed fuzzy LMS adaptive algorithm shows a better performance than than the LMS algorithm in terms of the convergence speed.
DOI:10.1109/ICSIGP.1996.567332