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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/11056

Title: 類神經學習向量量化網路之電子設備故障診斷
Neural Learning Vector Quantization Network for Electronic Equipment Fault Diagnosis
Authors: 魏忠必;陳家湧
Contributors: 電機工程系
Keywords: 類神經網路;學習向量量化網路;光電雷達;電子設備;故障診斷
Neural network;Learning vector quantization;Photovoltaic radar;Electronic equipment;Fault diagnosis
Date: 2006-03
Issue Date: 2012-06-05T03:52:01Z
Publisher: 銘傳大學;國際教育兼管理科學基金會
Abstract: 本文提出以類神經網路之學習向量量化網路(Learning Vector Quantization, LVQ)應用於光電雷達(Photovoltaic Radar)電子設備故障診斷(Electronic Equipment Fault Diagnosis)之研究。利用學習向量量化網路之特性,用已知的故障數據對類神經網路進行訓練後,即可對電子設備的故障型 態進行診斷。為驗證本文所提之LVQ對電子設備故障診斷之準確性,使用MATLAB撰寫LVQ程式,並經過訓練後診斷電壓或溫度資料,分辨出故障型態,以證明所提方法之效率及準確性。
This paper presents a Neural Network Learning Vector Quantization (LVQ) for photovoltaic radar electronic equipment fault diagnosis. Using the known data to train the LVQ Network. Then, input the new samples to diagnose the fault type. In order to prove the accuracy of the LVQ for electronic equipment fault diagnosis, using MATLAB to develop LVQ program. The simulation can prove the proposed method is effective and accurate.
Relation: 2006國際學術研討會-人工智慧理論及其應用, 銘傳大學, 2006年3月18日: 255-263
Appears in Collections:[電機工程學系] 會議論文

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