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Proceedings Paper

Comparative study between powers of sigmoid functions, MLP backpropagation, and polynomials in function approximation problems
Author(s): Joao Fernando Marar; Ana Claudia Patrocionio
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Paper Abstract

Function approximation is a very important task in environments where the computation has to be based on extracting information from data samples in real world processes. So, the development of new mathematical model is a very important activity to guarantee the evolution of the function approximation area. In this sense, we will present the Polynomials Powers of Sigmoid as a linear neural network. In this paper, we will introduce one series of practical results for the Polynomials Powers of Sigmoid, where we will show some advantages of the use of the powers of sigmoid functions in relationship the traditional MLP- Backpropagation and Polynomials in functions approximation problems.

Paper Details

Date Published: 27 July 1999
PDF: 8 pages
Proc. SPIE 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII, (27 July 1999); doi: 10.1117/12.357191
Show Author Affiliations
Joao Fernando Marar, Univ. Estadual Paulista (Brazil)
Ana Claudia Patrocionio, Univ. de Sao Paulo (Brazil)


Published in SPIE Proceedings Vol. 3720:
Signal Processing, Sensor Fusion, and Target Recognition VIII
Ivan Kadar, Editor(s)

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