淡江大學機構典藏:Item 987654321/46252
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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46252


    Title: Neural-network-based fuzzy model and its application to transient stability prediction in power systems
    Authors: 蘇木春;Su, Mu-chun;Liu, Chih-wen;Tsay, Shuenn-shing
    Contributors: 淡江大學電機工程學系
    Date: 1999-02
    Issue Date: 2010-03-26 21:42:19 (UTC+8)
    Publisher: Piscataway: Institute of Electrical and Electronics Engineers (IEEE)
    Abstract: We present a general approach to deriving a new type of neural network-based fuzzy model for a complex system from numerical and/or linguistic information. To efficiently identify the structure and the parameters of the new fuzzy model, we first partition the output space instead of the input space. As a result, the input space itself induces corresponding partitions within each of which inputs would have similar outputs. Then we use a set of hyperrectangles to fit the partitions of the input space. Consequently, the premise of an implication in the new type of fuzzy rule is represented by a hyperrectangle and the consequence is represented by a fuzzy singleton. A novel two-layer fuzzy hyperrectangular composite neural network (FHRCNN) can be shown to be computationally equivalent to such a special fuzzy model. The process of presenting input data to each hidden node in a FHRCNN is equivalent to firing a fuzzy rule. An efficient learning algorithm was developed to adjust the weights of an FHRCNN. Finally, we apply FHRCNNs to provide real-time transient stability prediction for use with high-speed control in power systems. From simulation tests on the IEEE 39-bus system, it reveals that the proposed novel FHRCNN can yield a much better performance than that of conventional multilayer perceptrons (MLP's) in terms of computational burden and classification rate
    Relation: IEEE transactions on systems, man and cybernetics, Part : C 29(1), pp.149-157
    DOI: 10.1109/5326.740677
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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