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Titolo:
Time series analysis using normalized PG-RBF network with regression weights
Autore:
Rojas, I; Pomares, H; Bernier, JL; Ortega, J; Pino, B; Pelayo, FJ; Prieto, A;
Indirizzi:
Univ Granada, Dept Architecture & Comp Technol, E-18071 Granada, Spain Univ Granada Granada Spain E-18071 Comp Technol, E-18071 Granada, Spain
Titolo Testata:
NEUROCOMPUTING
, volume: 42, anno: 2002,
pagine: 267 - 285
SICI:
0925-2312(200201)42:<267:TSAUNP>2.0.ZU;2-3
Fonte:
ISI
Lingua:
ENG
Soggetto:
PREDICTION;
Keywords:
RBF neural networks; sequential learning; pruning strategy; network growing; time series prediction;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
20
Recensione:
Indirizzi per estratti:
Indirizzo: Rojas, I Univ Granada, Dept Architecture & Comp Technol, Campus Univ Fuentenueva, E-18071 Granada, Spain Univ Granada Campus Univ Fuentenueva Granada Spain E-18071 Spain
Citazione:
I. Rojas et al., "Time series analysis using normalized PG-RBF network with regression weights", NEUROCOMPUT, 42, 2002, pp. 267-285

Abstract

This paper proposes a framework for constructing and training a radial basis function (RBF) neural network. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it ispossible to create a new hidden unit and also to detect and remove inactive units. The structure of the Gaussian functions is modified using a pseudo-Gaussian function (PG) in which two scaling parameters a are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with respect to function approximation. Other important characteristics of the proposed neural system are that theactivation of the hidden neurons is normalized which, as described in the bibliography, provides a better performance than nonnormalization and instead of using a single parameter for the output weights, these are functions of the input variables which leads to a significant reduction in the numberof hidden units compared to the classical RBF network. Finally, we examinethe result of applying the proposed algorithm to time series prediction. (C) 2002 Elsevier Science B.V. All rights reserved.

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Documento generato il 07/07/20 alle ore 18:56:33