Performance Analysis of Fractional Learning Algorithms

• URL: http://arxiv.org/abs/2110.05201v1
• Date: Mon, 11 Oct 2021 12:06:44 GMT
• Title: Performance Analysis of Fractional Learning Algorithms
• Authors: Abdul Wahab, Shujaat Khan, Imran Naseem, Jong Chul Ye
• Abstract summary: It is unclear whether the 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. Their origins and consequences on the performance of the learning algorithms are discussed and swift ready-witted remedies are proposed.
• Score: 32.21539962359158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fractional learning algorithms are trending in signal processing and adaptive filtering recently. However, it is unclear whether the 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.

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