Power signal classification using Adaptive Wavelet Network

Bebarta, Dwiti Krishna; Rout, Ajit Kumar; Biswal, Birendra; Biswal, Milan

doi:10.1109/icccct.2010.5670773

Cited by 1 publication

(2 citation statements)

References 6 publications

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\begin{matrix} right leftthickmathspace.5em \\ G (\frac{p - p_{j}}{σ}) = \frac{1}{{()(, 2 π σ^{2})}^{f_{0} / 2}} \exp ()(, \frac{{∥p - p_{j}∥}^{2}}{2 σ^{2}}); \\ j = 1, 2, 3, \dots, M \end{matrix}

where M is the total number of training patterns. Now, the input–output mapping function of the normalised RBF network takes the following form

ϕ false(p false) = \frac{\sum_{j = 1}^{M} d_{j} \exp ((- {(∥∥, p - p_{j})}^{2}) / (2 σ^{2}))}{\sum_{j = 1}^{M} \exp ((- {(∥∥, p - p_{j})}^{2}) / (2 σ^{2}))}

where d j is the linear weight applied to the basis function [15], and the denominator term represents the Parzen–Rosenblatt density estimator, which consists of the sum of ‘ M ’ multivariate Gaussian distribution centred on the data point p 1 , p 2 , p 3 , …, p M . In (23), the centres of the normalised RBF coincide with the data points

falsefalse{p_{j} falsefalse}_{j = 1}^{M}

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Section: Classification Using Pnnmentioning

confidence: 99%

“…Among the various classifiers, the neural network illustrates relatively efficient classification results, but suffers from trial and error approach of selecting number of hidden layers, training methods and so on. Among the various networks available, probabilistic neural network (PNN) [13–15] is the most popular one and has been effectively used to accurately identify various types of patterns. In PNN, the number of nodes in the hidden layer is equal to the number of training vectors.…”

Section: Introductionmentioning

confidence: 99%

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