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Proceedings Paper

Comparative study between RBF and radial-PPS neural networks
Author(s): Joao Fernando Marar; Edson C. B. Carvalho Filho; J. Dias dos Santos
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Paper Abstract

The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques.

Paper Details

Date Published: 25 March 1998
PDF: 10 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304830
Show Author Affiliations
Joao Fernando Marar, Univ. Estadual Paulista (Brazil)
Edson C. B. Carvalho Filho, Univ. Federal de Pernambuco (Brazil)
J. Dias dos Santos, Univ. Federal de Pernambuco (Brazil)


Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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