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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/88136

    Title: Fuzzy identification for Burn-In system
    Other Titles: 模糊鑑別器於Burn In系統之設計
    Authors: 陳光原;Chen, Kuang-Yuan
    Contributors: 淡江大學電機工程學系博士班
    江正雄;Chiang, Jen-Shiun
    Keywords: 模糊系統;群聚分析演算法:奔應系統;Fuzzy Systems;Clustering Algorithms;SOFM;Burn-In System
    Date: 2012
    Issue Date: 2013-04-13 12:02:24 (UTC+8)
    Abstract: 建立一個模糊系統主要是依靠專家經驗來提供設計方法。如果一個受控系統只知道它的輸入輸出資料時,如何在沒有專家經驗下來建立適當的模糊鑑別器是很大的挑戰。本論文會探討設計一個模糊鑑別器的方法,藉著學習法則來不斷地學習輸入輸出的資料的行為,使其儘可能趨近於欲鑑別的系統。
    在論文的第二部份,我們介紹一種基於競爭式學習的模糊系統建模方法。我們利用SOFM 神經網路在低維度矩陣空間內輸出拓樸網路來得到高維度輸入資料的推理法則,並產生有意義的規則資料庫以重塑受鑑別系統。而為了更精確逼近受鑑系統的特性,本論文將萃取出的模糊規則進一步結合遞迴式最小平方法進行參數鑑別的設計程序,來達到微調的效果。
    To establish a fuzzy system is to rely on the experience of an expert to provide the design methods. For a controlled system, if we only know its input-output data, it would be a challenge to establish an appropriate fuzzy system without expert experience.
    The first task of the dissertation is to develop the fuzzy space clustering algorithm. It can effectively explore the cluster distribution of the processed data and analyze the clustering of the input-output data of the identified system. We can use the input-output data of the identified system as a training target to tune the parameters of the fuzzy systems more precisely in line with the behavior of the identified system.
    The second task of the dissertation is to introduce a competitive learning fuzzy system modeling approach.We use the topological network sent out from SOFM to get the meaningful fuzzy rules. We can use the input-output data as the training samples to further fine-tune the parameter of fuzzy inference system so that it can more accurately match the behavior of the system to be identified.
    The last task of the dissertation is to apply the fuzzy indenitification design to approach a thermostatically controlled Burn-In test system. In this Burn-In test system, we need to control the heater and fan to keep the temperature stabilized in the set range. We use the propsed fuzzy identification method to build a fuzzy controller to manipulate the operaion of the fan and heater. The actual results show the method can reduce the time of trial-and-error for the parameter adjustment. The system also has faster rising time and smaller overshoot.
    Appears in Collections:[電機工程學系暨研究所] 學位論文

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