An incremental adaptive implementation of functional-link processing for function approximation, time-series prediction, and system identification

This paper presents an adaptive implementation of the functional-link neural network (FLNN) architecture together with a supervised learning algorithm that rapidly determines the weights of the network. The proposed algorithm is able to achieve ‘one-shot’ training as opposed to iterative training al...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 1998, Vol.18 (1), p.11-31
Main Authors: Chen, C.L.Philip, LeClair, Steven R., Pao, Yoh-Han
Format: Article
Language:English
Subjects:
Forecasting
Function approximations
Functional-link neural network
Instant learning algorithm
Least squares
System identification
Time-series
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Summary:This paper presents an adaptive implementation of the functional-link neural network (FLNN) architecture together with a supervised learning algorithm that rapidly determines the weights of the network. The proposed algorithm is able to achieve ‘one-shot’ training as opposed to iterative training algorithms in the literature. Also discussed is a stepwise updating algorithm that updates the weights of the network while importing new observations. The proposed algorithms have also been tested on several data sets and the simulation shows a very promising result.
ISSN:0925-2312
1872-8286
DOI:10.1016/S0925-2312(97)00062-3