Performance Analysis of Fractional Learning Algorithms
This addresses the need for clarity in signal processing and adaptive filtering on whether fractional algorithms offer real benefits, though it appears incremental as it analyzes existing variants rather than introducing a new method.
The paper tackled the problem of whether fractional learning algorithms are truly superior to conventional ones by performing a rigorous analysis of fractional variants of least mean squares and steepest descent algorithms, identifying critical schematic kinks and proposing remedies, with numerical experiments discussing convergence and efficiency in stochastic environments.
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.