The proposed spatio-temporal RBF network reduces, by its own equations, to a standard RBF with reindexed hidden units, making the reported accuracy gain an artifact of hyperparameter choices rather than a new architecture.
Comments on "Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization"
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abstract
The purpose of this note is to discuss some aspects of recently proposed fractional-order variants of complex least mean square (CLMS) and normalized least mean square (NLMS) algorithms in ``Design of Fractional-order Variants of Complex LMS and Normalized LMS Algorithms for Adaptive Channel Equalization'' [Nonlinear Dyn. 88(2), 839-858 (2017)]. It is observed that these algorithms do not always converge whereas they have apparently no advantage over the CLMS and NLMS algorithms whenever they converge. Our claims are based on analytical reasoning and are supported by numerical simulations.
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Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks
The proposed spatio-temporal RBF network reduces, by its own equations, to a standard RBF with reindexed hidden units, making the reported accuracy gain an artifact of hyperparameter choices rather than a new architecture.