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.
翻译:分数学习算法在最近逐渐趋向于信号处理和适应性过滤,然而,尚不清楚所宣布的优先于常规算法的优越性是否理由充分,或是否是一个神话,因为其性能从未广泛分析过。在本条中,对最不中性的平方和最陡峭的下行算法的分数变异进行了严格分析。确定了分数学习算法中的一些关键示意图。讨论了这些算法的起源和对学习算法表现的影响,并提出了迅速的现成补救办法。进行了应用性数字试验,以讨论分数学习算法在随机环境中的趋同和效率。