A logical architecture for supervised learning
The author discusses a neural network architecture for supervised learning with inherent stability properties. The architecture uses two ART 1 unsupervised systems with supervision through interconnects. The system will respond as trained under controlled operating conditions, and the use of adaptiv...
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| Published in | IEEE-INNS International Joint Conference on Neural Networks - Singapore, 1991 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
01.01.1992
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| Online Access | Get full text |
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| Summary: | The author discusses a neural network architecture for supervised learning with inherent stability properties. The architecture uses two ART 1 unsupervised systems with supervision through interconnects. The system will respond as trained under controlled operating conditions, and the use of adaptive resonance provides a means of addressing novel inputs. This and the ability to apply the network as a knowledge system are aided by a formal logic model. The system has potential applications in multisensor analysis, adaptive control, and neural network knowledge systems. |
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| Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| ISBN: | 0780302273 9780780302273 |