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Designing Optimal Knowledge Base for Neural Expert Systems

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  • Dat-Dao Nguyen

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.

Suggested Citation

  • Dat-Dao Nguyen, 2017. "Designing Optimal Knowledge Base for Neural Expert Systems," International Business Research, Canadian Center of Science and Education, vol. 10(6), pages 137-144, June.
    • Handle: RePEc:ibn:ibrjnl:v:10:y:2017:i:6:p:137-144
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    Cited by:

    1. Luis Enrique Valdez Juarez & Elba Alicia Ramos Escobar & Gonzalo Maldonado Guzman, 2017. "The Effects of Absorptive Capacity, Intellectual Property and Innovation in SMEs," Journal of Management and Sustainability, Canadian Center of Science and Education, vol. 7(4), pages 36-50, December.

    More about this item

    Keywords

    decision rule; expert systems; neural networks; knowledge acquisition; knowledge representation; knowledge-based systems;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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