Title :
Feedforward neural networks based on PPS-wavelet activation functions
Author :
Marar, João Fernando ; Filho, Edson Costa B C ; Vasconcelos, Germano Crispim
Author_Institution :
Dept. de Computacao, Univ. Estadual Paulista, Sao Paulo, Brazil
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 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 (polynomial powers of sigmoids)-wavelet. An algorithm is presented to generate a family of PPS-wavelets that can be used to efficiently construct feedforward networks for function approximation
Keywords :
feedforward neural nets; function approximation; polynomials; transfer functions; wavelet transforms; PPS-wavelet activation functions; feedforward neural networks; function approximation; polynomial powers of sigmoids-wavelet; Approximation algorithms; Data mining; Feedforward neural networks; Function approximation; Hilbert space; Multidimensional systems; Multilayer perceptrons; Neural networks; Polynomials; Robustness;
Conference_Titel :
Cybernetic Vision, 1996. Proceedings., Second Workshop on
Conference_Location :
Sao Carlos
Print_ISBN :
0-8186-8058-X
DOI :
10.1109/CYBVIS.1996.629472