Comparative study between RBF and radial-PPS neural networks

Joao Fernando Marar; Edson C. B. Carvalho Filho; J. Dias dos Santos

doi:10.1117/12.304830

25 March 1998 Comparative study between RBF and radial-PPS neural networks

Joao Fernando Marar, Edson C. B. Carvalho Filho, J. Dias dos Santos

Proceedings Volume 3390, Applications and Science of Computational Intelligence; (1998) https://doi.org/10.1117/12.304830
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

Citation Download Citation

Joao Fernando Marar, Edson C. B. Carvalho Filho, and J. Dias dos Santos "Comparative study between RBF and radial-PPS neural networks", Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); https://doi.org/10.1117/12.304830

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