Feedforward neural networks based on PPS-wavelet activation functions

Marar, Joao Fernando [UNESP]; Filho, Edson Costa B.C. [UNESP]; Vasconcelos, Germano Crispim [UNESP]

Feedforward neural networks based on PPS-wavelet activation functions

Date

1997-12-01

Authors

Marar, Joao Fernando

Filho, Edson Costa B.C.

Vasconcelos, Germano Crispim

Type

Work presented at event

Abstract

Function 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.