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.
