Multidimensional Polynomial Powers of Sigmoid (PPS) wavelet neural networks

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dc.contributor.authorMarar, João Fernando [UNESP]
dc.contributor.authorCoelho, Helder
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionLisbon University
dc.date.accessioned2022-04-28T20:45:05Z
dc.date.available2022-04-28T20:45:05Z
dc.date.issued2008-11-13
dc.description.abstractWavelet functions have been used as the activation function in feedforward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical backpropagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As an example of application for the method proposed, it is studied the exclusive-or (XOR) problem.en
dc.description.affiliationDepartment of Computing Faculdade de Ciências São Paulo State University, Bauru, São Paulo
dc.description.affiliationDepartment of Informatics Faculdade de Ciências Lisbon University, Lisbon
dc.description.affiliationUnespDepartment of Computing Faculdade de Ciências São Paulo State University, Bauru, São Paulo
dc.format.extent261-268
dc.identifier.citationBIOSIGNALS 2008 - Proceedings of the 1st International Conference on Bio-inspired Systems and Signal Processing, v. 2, p. 261-268.
dc.identifier.scopus2-s2.0-55649110670
dc.identifier.urihttp://hdl.handle.net/11449/225323
dc.language.isoeng
dc.relation.ispartofBIOSIGNALS 2008 - Proceedings of the 1st International Conference on Bio-inspired Systems and Signal Processing
dc.sourceScopus
dc.subjectActivation functions
dc.subjectArtificial neural network
dc.subjectFeedforward networks
dc.subjectFunction approximation
dc.subjectPolynomial Powers of Sigmoid (PPS)
dc.subjectPPS-wavelet neural networks
dc.subjectWavelets functions
dc.titleMultidimensional Polynomial Powers of Sigmoid (PPS) wavelet neural networksen
dc.typeTrabalho apresentado em evento
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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