This paper presents a fuzzy step-size LMS adaptive filter. This method allows the adaptive filter to converge faster and to produce smaller steady state errors compared to the fixed step-size LMS adaptive filter. Furthermore, it provides a general framework where the user can incorporate additional safety rules or conditions that will prevent the adaptive algorithm to become unstable and to react to sudden changing conditions more rapidly. Simulation results are presented to compare the performance of the new approach with the fixed step-size LMS algorithm and other commonly used variable step-size LMS algorithms such as those proposed by Harris et al. (1986) and Kwong and Johnston (1992). [References: 5]
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