a novel kernel for rbf based neural networks

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ID: 241917
2014
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Abstract
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.
Reference Key
aftab2014abstracta Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Wasim Aftab;Muhammad Moinuddin;Muhammad Shafique Shaikh
Journal science and technology of advanced materials
Year 2014
DOI
10.1155/2014/176253
URL
http://dx.doi.org/10.1155/2014/176253
Keywords
mathematics

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