本文選題：液壓信號 + EMD��； 參考：《液壓與氣動》2015年11期

【摘要】：液壓信號具有非平穩(wěn)性、非線性、特征信息相近時難以正確辨識的特點。針對該特點提出了一種經驗模態(tài)分解(EMD)和多特征組合的信號辨識方法。該方法將信號自適應分解為若干個固有模態(tài)函數(IMF);提取各IMF分量的能量、裕度、峰度、波動系數等特征參數,規(guī)范化后組合形成全局特征向量,并輸入支持向量機(SVM)中學習和辨識。通過對液壓主管壓力信號處理表明:該方法能有效辨識特征信息相近的壓力信號,在小樣本下仍然具有較好的辨識率。
[Abstract]:Hydraulic signals are non-stationary, nonlinear and difficult to identify correctly when the characteristic information is close. A signal identification method based on empirical mode decomposition (EMD) and multi-feature combination is proposed. The method decomposes the signal adaptively into several inherent mode functions, extracts the energy, margin, kurtosis and fluctuation coefficient of each IMF component, and forms a global eigenvector after normalization. And input support vector machine (SVM) learning and identification. The pressure signal processing of hydraulic supervisor shows that the method can effectively identify the pressure signal with similar characteristic information, and it still has a good identification rate under small samples.
【作者單位】： 武漢科技大學信息科學與工程學院;
【基金】：國家自然科學基金(61174106)
【分類號】：TG333.1
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本文編號：1923489