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

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contributor.authorDash, P K-
contributor.authorNanda, P K-
contributor.authorSaha, S-
contributor.authorDoraiswami, R-
identifier.citationProceedings of the First International Forum on Applications of Neural Networks to Power Systems, 23-26 July 1991, Seattle, WA P 288-292en
descriptionPersonal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en
description.abstractEstimation 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 other two algorithms. Simulation results for a wide variety of cases are presented for better appraisalen
format.extent361366 bytes-
subjectdigital simulationen
subjectfeedforward neural netsen
subjectpattern recognitionen
titleArtificial neural network & pattern recognition approach for narrowband signal extractionen
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