A novel kernel for RBF based neural networks
type: Item
instance of: scholarly article
title: A novel kernel for RBF based neural networks
zbMATH Open document ID: 1470.68066
DOI: 10.1155/2014/176253
author: Wasim Aftab
author: Muhammad Moinuddin
author: Muhammad Shafique Shaikh
published in: Abstract and Applied Analysis
publication date: 2019-02-14T00:00:00Z
review text: Summary: Radial basis function (RBF) is well known to provide excellent performance in function approximation and pattern classification. The conventional RBF uses basis functions which rely on distance measures such as Gaussian kernel of Euclidean distance (ED) between feature vector and neuron's center, and so forth. In this work, we introduce a novel RBF artificial neural network (ANN) where the basis function utilizes a linear combination of ED based Gaussian kernel and a cosine kernel where the cosine kernel computes the angle between feature and center vectors. Novelty of the proposed work relies on the fact that we have shown that there may be scenarios where the two feature vectors (FV) are more prominently distinguishable via the proposed cosine measure as compared to the conventional ED measure. We discuss adaptive symbol detection for multiple phase shift keying (MPSK) signals as a practical example to show where the angle information can be pivotal which in turn justifies our proposed RBF kernel. To corroborate our theoretical developments, we investigate the performance of the proposed RBF for the problems pertaining to three different domains. Our results show that the proposed RBF outperforms the conventional RBF by a remarkable margin.
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 7021871
describes a project that uses: Flavia
describes a project that uses: Silhouettes
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/176253
OpenAlex ID: W2115872170
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description: scientific article; zbMATH DE number 7021871