Multi-Kernel Fusion for RBF Neural Networks

Abstract

A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks. The current generation of radial basis function neural network is equipped with multiple kernels which provide significant performance benefits compared to the previous generation using only a single kernel. In existing multi-kernel RBF algorithms, multi-kernel is formed by the convex combination of the base/primary kernels. In this paper, we propose a novel multi-kernel RBFNN in which every base kernel has its own (local) weight. This novel flexibility in the network provides better performance such as faster convergence rate, better local minima and resilience against stucking in poor local minima. These performance gains are achieved at a competitive computational complexity compared to the contemporary multi-kernel RBF algorithms. The proposed algorithm is thoroughly analysed for performance gain using mathematical and graphical illustrations and also evaluated on three different types of problems namely: (i) pattern classification, (ii) system identification and (iii) function approximation. Empirical results clearly show the superiority of the proposed algorithm compared to the existing state-of-the-art multi-kernel approaches.

Anticancer Peptides Classification Using Kernel Sparse Representation Classifier

“…Technical advances in artificial intelligence (AI) have substantiated that it is a powerful tool for dealing with incredibly complex situations [7]. Many studies have used machine learning models to predict proteins and classify peptide sequences; see, for instance, [8]- [13].…”

“…In this regard, Zhang et al proposed a kernel SRC [43]. The kernel mapping converts the nonlinear relationship between different atoms (samples in OCD) to a linear relationship, allowing the classification of even more complex patterns [7], [43], [44]. Furthermore, a composition of K-spaced amino-acid pairs (CKSAAP) is employed to capture a diverse range of peptide sequences, yielding a comprehensive feature vector.…”