2018/01/01 by Shujaat Khan, Khan, Shujaat, Alishba Sadiq +7 · 2 citations
Engineering · Computer Science · #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #Speech and Audio Processing
paper · pdf · doi:10.48550/arxiv.1801.00410
In this work, a new class of stochastic gradient algorithm is developed based on q-calculus. Unlike the existing q-LMS algorithm, the proposed approach fully utilizes the concept of q-calculus by incorporating time-varying q parameter. The proposed enhanced q-LMS (Eq-LMS) algorithm utilizes a novel, parameterless concept of error-correlation energy and normalization of signal to ensure high convergence, stability and low steady-state error. The proposed algorithm automatically adapts the learning rate with respect to the error. For the evaluation purpose the system identification problem is considered. Extensive experiments show better performance of the proposed Eq-LMS algorithm compared to the standard q-LMS approach.