Performance Analysis of Fractional Learning Algorithms
Wahab A. Khan S. Naseem I. Ye J.C.
2022Institute of Electrical and Electronics Engineers Inc.
IEEE Transactions on Signal Processing
2022#705164 - 5177 pp.
Fractional learning algorithms are trending in signal processing and adaptive filtering recently. However, it is unclear whether their proclaimed superiority over conventional algorithms is well-grounded or is a myth as their performance has never been extensively analyzed. In this article, a rigorous analysis of fractional variants of the least mean squares and steepest descent algorithms is performed. Some critical schematic kinks in fractional learning algorithms are identified. Their origins and consequences on the performance of the learning algorithms are discussed and swift ready-witted remedies are proposed. Apposite numerical experiments are conducted to discuss the convergence and efficiency of the fractional learning algorithms in stochastic environments. The analysis substantiates that the fractional learning algorithms have no advantage over the conventional least mean squares algorithm.
fractional derivatives , fractional least mean squares , gradient descent , Least mean squares
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Department of Mathematics, School of Sciences and Humanities, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
Department of Bio and Brain Engineering, KoreaAdvanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
School of Electrical, Electronic and Computer Engineering, University of Western Australia, Crawley, 6009, WA, Australia
PAF Karachi Institute of Economics and Technology, College of Engineering, Korangi Creek, 75190, Pakistan
Korea Advanced Institute of Science and Technology (KAIST), Kim Jaechul Graduate School of AI, Daejeon, 34141, South Korea
Department of Mathematics
Department of Bio and Brain Engineering
School of Electrical
PAF Karachi Institute of Economics and Technology
Korea Advanced Institute of Science and Technology (KAIST)
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