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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wu, Q. McGinnity, T.M. Prasad, G. Bell, D. |
| Copyright Year | 2005 |
| Description | Author affiliation: Sch. of Comput. & Intelligent Syst., Ulster Univ., Londonderry, UK (Wu, Q.; McGinnity, T.M.; Prasad, G.) |
| Abstract | In this paper, an evolving learning mechanism is proposed for general computing network model to make decisions in intelligent systems. The novel mechanism is performed by means of a set of computing cell operations such as self-generation, growth, self-division, and death. Under the mechanism, a computing network grows up to a mature network. A hidden cell in the network is defined as a condition matching-unit in response to a fuzzy sub-superspace in multiple-dimension input superspace. A sense-function is defined to represent connections from a hidden cell to input cells. The range and edge vagueness of the sense-function are determined by evolving learning mechanism when sample instances are presented to the network. This network is able to learn from a very few training instances to make decisions for unseen instances. The benchmark data sets from the UCI machine learning repository are applied to test the network and comparable results are obtained. |
| Starting Page | 1914 |
| Ending Page | 1919 |
| File Size | 282512 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780392981 |
| DOI | 10.1109/ICSMC.2005.1571426 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-10-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Learning systems Computer networks Neural networks Information systems Artificial intelligence Intelligent systems Intelligent networks Evolution (biology) Intelligent robots Neurons decision-making Computing network model evolving learning mechanism intelligent system |
| Content Type | Text |
| Resource Type | Article |
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