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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| Veröffentlicht in: | Neurocomputing (Amsterdam) 1998, Vol.18 (1), p.11-31 |
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| container_end_page | 31 |
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| container_title | Neurocomputing (Amsterdam) |
| container_volume | 18 |
| creator | Chen, C.L.Philip LeClair, Steven R. Pao, Yoh-Han |
| description | 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. |
| doi_str_mv | 10.1016/S0925-2312(97)00062-3 |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 0925-2312 |
| ispartof | Neurocomputing (Amsterdam), 1998, Vol.18 (1), p.11-31 |
| issn | 0925-2312 1872-8286 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_26660218 |
| source | Elsevier ScienceDirect Journals |
| subjects | Forecasting Function approximations Functional-link neural network Instant learning algorithm Least squares System identification Time-series |
| title | An incremental adaptive implementation of functional-link processing for function approximation, time-series prediction, and system identification |
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