Feedforward neural networks based on PPS-wavelet activation functions

Página do item simplificado
dc.contributor.authorMarar, Joao Fernando [UNESP]
dc.contributor.authorFilho, Edson Costa B.C. [UNESP]
dc.contributor.authorVasconcelos, Germano Crispim [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-04-29T08:46:54Z
dc.date.available2022-04-29T08:46:54Z
dc.date.issued1997-12-01
dc.description.abstractFunction approximation is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. Neural networks and wavenets have been recently seen as attractive tools for developing efficient solutions for many real world problems in function approximation. In this paper, it is shown how feedforward neural networks can be built using a different type of activation function referred to as the PPS-wavelet. An algorithm is presented to generate a family of PPS-wavelets that can be used to efficiently construct feedforward networks for function approximation.en
dc.description.affiliationUNESP- Universidade Estadual Paulista, Sao Paulo
dc.description.affiliationUnespUNESP- Universidade Estadual Paulista, Sao Paulo
dc.format.extent240-245
dc.identifier.citationProceedings of the Workshop on Cybernetic Vision, p. 240-245.
dc.identifier.scopus2-s2.0-0031380571
dc.identifier.urihttp://hdl.handle.net/11449/231682
dc.language.isoeng
dc.relation.ispartofProceedings of the Workshop on Cybernetic Vision
dc.sourceScopus
dc.titleFeedforward neural networks based on PPS-wavelet activation functionsen
dc.typeTrabalho apresentado em evento
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

Arquivos

Coleções

Bauru - FC - Faculdade de Ciências