Multidimensional Polynomial Powers of Sigmoid (PPS) wavelet neural networks

• Autor(a) principal: Marar, João Fernando [UNESP]
• Data de Publicação: 2008
• Outros Autores: Coelho, Helder
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/225323
• topic: Activation functions; Artificial neural network; Feedforward networks; Function approximation; Polynomial Powers of Sigmoid (PPS); PPS-wavelet neural networks; Wavelets functions

Wavelet 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.