The performance comparison of improved continuous mixed P-norm and other adaptive algorithms in sparse system identification

توسط

نویسندگان: افسانه اخباری – ابوذر غفاری

سال: ۱۳۹۵

زبان: انگلیسی

سومين كنفرانس بين المللي مهندسي دانش بنيان و نوآوري (KBEI-2016)

کلمات کلیدی:

Adaptive algorithms – sparse – mixed P-norm – system identification

چکیده:

One of the important usages of adaptive filters is in sparse system identification on which the performance of classic adaptive filters is not acceptable. There are several algorithms that designed especially for sparse systems, we call them sparsity aware algorithms. In this paper we studied the performance of two newly presented adaptive algorithms in which P-norm constraint is considered in defining cost function. The general name of these algorithms is continuous mixed P-norm (CMPN). The performances of these algorithms are considered for the first time in sparse system identification. Also the performance of 𝒍𝟎 norm LMS algorithm is analyzed and compared with our proposed algorithms. The performance analyses are carried out with the steady-state mean square deviation (MSD) criterion of adaptive algorithms. We hope that this work will inspire researchers to look for other advanced algorithms against systems that are sparse

 

 

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