In this research, we propose a novel fractional gradient descent-based learning algorithm
(FGD) for the radial basis function neural networks (RBF-NN). The proposed FGD is the …

In this work, a new class of stochastic gradient algorithm is developed based on q-calculus.
Unlike the existing q-LMS algorithm, the proposed approach fully utilizes the concept of q …

A simple yet effective architectural design of radial basis function neural networks (RBFNN)
makes them amongst the most popular conventional neural networks. The current …

Herein, we propose a new class of stochastic gradient algorithm for channel identification.
The proposed q-least mean fourth (q-LMF) is an extension of the least mean fourth (LMF) …

Due to the dynamic nature, chaotic time series are difficult predict. In conventional signal
processing approaches signals are treated either in time or in space domain only. Spatio …

Deep learning model is a multi-layered network structure, and the network parameters that
evaluate the final performance of the model must be trained by a deep learning optimizer. In …

In this paper, we propose an adaptive framework for the variable power of the fractional least
mean square (FLMS) algorithm using the concept of instantaneous error energy. The …

As the communication systems have been increasingly complex, the problem of channel
estimation for such complex communication systems has also emerged as an equally …

A novel adaptive filtering method called q-Volterra least mean square (q-VLMS) is presented
in this paper. The q-VLMS is a nonlinear extension of conventional LMS and it is based on …

Abstract The Least Mean Square (LMS) algorithm has a slow convergence rate as it is
dependent on the eigenvalue spread of the input correlation matrix. In this research, we …