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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/74

Title: Artificial neural network & pattern recognition approach for narrowband signal extraction
Authors: Dash, P K
Nanda, P K
Saha, S
Doraiswami, R
Keywords: backpropagation
digital simulation
feedforward neural nets
pattern recognition
Issue Date: 1991
Publisher: IEEE
Citation: Proceedings of the First International Forum on Applications of Neural Networks to Power Systems, 23-26 July 1991, Seattle, WA P 288-292
Abstract: Estimation of unknown frequency, extraction of narrowband signals buried under noise and periodic interference are accomplished by employing existing techniques. However, the authors propose an artificial neural net based scheme together with pattern classification algorithm for narrowband signal extraction. A three layer feedforward net is trained with three different algorithms namely backpropagation, Cauchy's algorithm with Boltzmann's probability distribution feature and the combined backpropagation-Cauchy's algorithm. A constrained tangent hyperbolic function is used to activate individual neurons. Computer simulation is carried out with inadequate data to reinforce the idea of the net's generalization capability. The robustness of the proposed scheme is claimed with the results obtained by making 25% links faulty between the layers. Performance comparison of the three algorithms is made and the superiority of the combined backpropagation-Cauchy's algorithm is established over the...
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URI: http://hdl.handle.net/2080/74
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