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Found 11 papers, 5 papers with code
Anticancer Peptides Classification using Kernel Sparse Representation Classifier
In this paper, we present an intuitive classification strategy that differs from the traditional \emph{black box} method and is based on the well-known statistical theory of \emph{sparse-representation classification} (SRC).
Performance Analysis of Fractional Learning Algorithms
Fractional learning algorithms are trending in signal processing and adaptive filtering recently.
q-RBFNN:A Quantum Calculus-based RBF Neural Network
In this research a novel stochastic gradient descent based learning approach for the radial basis function neural networks (RBFNN) is proposed.
Multi-Kernel Fusion for RBF Neural Networks
A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks.
Heart Segmentation From MRI Scans Using Convolutional Neural Network
Heart is one of the vital organs of human body.
Spatio-Temporal RBF Neural Networks
Herein, we propose a spatio-temporal extension of RBFNN for nonlinear system identification problem.
A Novel Adaptive Kernel for the RBF Neural Networks
In this paper, we propose a novel adaptive kernel for the radial basis function (RBF) neural networks.
q-LMF: Quantum Calculus-based Least Mean Fourth Algorithm
The proposed $q$-least mean fourth ($q$-LMF) is an extension of least mean fourth (LMF) algorithm and it is based on the $q$-calculus which is also known as Jackson derivative.
RAFP-Pred: Robust Prediction of Antifreeze Proteins using Localized Analysis of n-Peptide Compositions
In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs) to counter the otherwise lethal intracellular formation of ice.
Comments on "Momentum fractional LMS for power signal parameter estimation"
The purpose of this paper is to indicate that the recently proposed Momentum fractional least mean squares (mFLMS) algorithm has some serious flaws in its design and analysis.
Comments on "Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization"
The purpose of this note is to highlight some critical flaws in 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" [Non-linear Dyn.
Optimization and Control
