Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification
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Data
2014-01-01
Autores
Pereira, Luís Augusto Martins [UNESP]
Rodrigues, Douglas [UNESP]
Ribeiro, Patricia Bellin [UNESP]
Papa, João Paulo
Weber, Silke Anna Theresa [UNESP]
Título da Revista
ISSN da Revista
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Editor
Ieee
Resumo
Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.
Descrição
Palavras-chave
Artificial Neural Networks, Parkinsons' Disease, Social-Spider Optimization
Como citar
2014 Ieee 27th International Symposium On Computer-based Medical Systems (cbms). New York: Ieee, p. 14-17, 2014.