 Designing Optimal Knowledge Base for Neural Expert SystemsDat-Dao Nguyen International Business Research, 2017, vol. 10, issue 6, 137-144 Abstract: One of the limitations of conventional expert systems and traditional machine induction methods in capturing human expertise is in their requirement of a large pool of structured samples from a multi-criteria decision problem domain. Then the experts may have difficulty in expressing explicitly the rules on how each decision was reached. To overcome these shortcomings, this paper reports on the design of an optimal knowledge base for machine induction with the integration of Artificial Neural Network (ANN) and Expert Systems (ES). In this framework, an orthogonal plan is used to define an optimal set of examples to be taken. Then holistic judgments of experts on these examples will provide a training set for an ANN to serve as an initial knowledge base for the integrated system. Any counter-examples in generalization over new cases will be added to the training set to retrain the network to enlarge its knowledge base. Keywords: decision rule; expert systems; neural networks; knowledge acquisition; knowledge representation; knowledge-based systems (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ibn:ibrjnl:v:10:y:2017:i:6:p:137-144 Access Statistics for this article More articles in International Business Research from Canadian Center of Science and Education Contact information at EDIRC. |
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