Connectionist expert system with adaptive learning capability
| Autor: | Ah-Hwee Tan, B.T. Low, Ho-Chung Lui, H.H. Teh |
|---|---|
| Rok vydání: | 1991 |
| Předmět: |
Artificial neural network
business.industry Computer science Hardware_PERFORMANCEANDRELIABILITY Connectionist expert system computer.software_genre Machine learning Knowledge acquisition Expert system Computer Science Applications Computational Theory and Mathematics Connectionism Adaptive system Adaptive learning Artificial intelligence Pattern matching business computer Logic programming Information Systems |
| Zdroj: | IEEE Transactions on Knowledge and Data Engineering. 3:200-207 |
| ISSN: | 1041-4347 |
| Popis: | A neural network expert system called adaptive connectionist expert system (ACES) which will learn adaptively from past experience is described. ACES is based on the neural logic network, which is capable of doing both pattern processing and logical inferencing. The authors discuss two strategies, pattern matching ACES and rule inferencing ACES. The pattern matching ACES makes use of past examples to construct its neural logic network and fine-tunes itself adaptively during its use by further examples supplied. The rule inferencing ACES conceptualizes new rules based on the frequencies of use on the rule-based neural logic network. A new rule could be considered as a pattern matching example and be incorporated into pattern matching ACES. > |
| Databáze: | OpenAIRE |
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