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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/38758

    Title: A neural networks based approach to knowledge aquistion and expert systems
    Authors: DeClaris, Nicholas;蘇木春;Su, Mu-chun
    Contributors: 淡江大學電機工程學系
    Date: 1993-10-17
    Issue Date: 2010-04-15 10:52:42 (UTC+8)
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Abstract: Often a major difficulty in the design of expert systems is the process of acquiring the requisite knowledge in the form of production rules. This paper presents a novel class of neural networks which are trained in such a way that they provide an appealing solution to the problem of knowledge acquisition. The value of the network parameters, after sufficient training, are then utilized to generate production rules on the basis of preselected meaningful coordinates. Further, the paper provides a mathematical framework for achieving reasonable generalization properties via an appropriate training algorithm (supervised decision-directed learning) with a structure that provides acceptable knowledge representations of the data, The concepts and methods presented in the paper are illustrated through one practical example from medical diagnosis.
    Relation: Systems, Man and Cybernetics, 1993. 'Systems Engineering in the Service of Humans', Conference Proceedings., International Conference on (Volume:2 ), pp.645-650
    DOI: 10.1109/ICSMC.1993.384948
    Appears in Collections:[電機工程學系暨研究所] 會議論文

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