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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46192


    Title: Application of neural networks incorporated with real-valued genetic algorithms in knowledge acquisition
    Authors: 蘇木春;Su, Mu-chun;Chang, Hsiao-te
    Contributors: 淡江大學電機工程學系
    Keywords: Neural networks;Pattern recognition;Medicine;Fuzzy control
    Date: 2000-05-01
    Issue Date: 2010-03-26 21:11:11 (UTC+8)
    Publisher: Elsevier
    Abstract: Often a major difficulty in the design of rule-based systems is the process of acquiring the requisite knowledge in the form of If–Then rules. This paper presents a class of fuzzy degraded hyperellipsoidal composite neural networks (FDHECNNs) that are trained to provide appealing solutions to the problem of knowledge acquisition. The values of the network parameters, after sufficient training, are then utilized to generate If–Then rules on the basis of preselected meaningful features. In order to avoid the risk of getting stuck in local minima during the training process, a real-valued genetic algorithm is proposed to train FDHECNNs. The effectiveness of the method is demonstrated on two problems, namely, the “truck backer-upper” problem as well as real-world application of a hypothesis regarding the pathophysiology of diabetes.
    Relation: Fuzzy sets and systems 112(1), pp.85-97
    DOI: 10.1016/S0165-0114(98)00180-8
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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