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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/95971

    Title: A Real-Valued GA-Based Approach to Extracting Control Fuzzy Rules
    Authors: Su, Mu-Chun;Chang, Hsiao-Te;Yu, Hua-Chiao
    Contributors: 淡江大學電機工程學系
    Keywords: 遺傳演算法;模糊邏輯控制器;神經-模糊系統;專家系統;Genetic Algorithm;Fuzzy Logic Controller;Neuro-Fuzzy System;Expert System
    Date: 1996-04
    Issue Date: 2014-02-13 11:28:51 (UTC+8)
    Abstract: In this paper, we present a neuro-fuzzy approach to design a controller directly from numerical data. The proposed neuro-fuzzy system is implemented as a two-layer Fuzzy Degraded HyperEllipsoidal Composite Neural Network(FDHECNN). We used a real-valued genetic algorithm to adjust weights of the composite neural networks. After sufficient training, the synaptic weights of the trained FDHECNN can be utilized to extract a set of fuzzy if-then rules. The performance of a trained FDHECNN is shown to be computationally identical to a fuzzy logic controller. The effectiveness and feasibility of the neuro-fuzzy system are tested on the truck backer-upper control problem.
    Relation: 一九九六自動控制研討會暨兩岸機電及控制技術交流學術研討會論文集=Proceedings of 1996 Automatic Control Conference,頁289-294
    Appears in Collections:[電機工程學系暨研究所] 會議論文

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