Chu Kiong, L. and Rajeswari, M. and Rao, M.V.C. (2003) Extrapolation detection and novelty-based node insertion for sequential growing multi-experts network. Applied Soft Computing, 3 (2). pp. 159-175. ISSN 1568-4946,
Full text not available from this repository.Abstract
Artificial neural networks (ANNs) have been used to construct empirical nonlinear models of process data. Because networks are not based on physical theory and contain nonlinearities, their predictions are suspect when extrapolating beyond the range of original training data. Standard networks give no indication of possible errors due to extrapolation. This paper describes a sequential supervised learning scheme for the recently formalized Growing multi-experts network (GMN). It is shown that certainty factor can be generated by GMN that can be taken as extrapolation detector for GMN. On-line GMN identification algorithm is presented and its performance is evaluated. The capability of the GMN to extrapolate is also indicated. Four benchmark experiments are dealt with to demonstrate the effectiveness and utility of GMN as a universal function approximator.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Extrapolation; Certainty factor; Novelty-based insertion; On-line learning |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Miss Nur Jannatul Adnin Ahmad Shafawi |
| Date Deposited: | 19 Mar 2013 00:28 |
| Last Modified: | 19 Mar 2013 00:28 |
| URI: | http://eprints.um.edu.my/id/eprint/5161 |
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