發(fā)布時間：2018-06-02 16:52

  本文選題：模糊樹 + 局部異常因子��； 參考：《動力工程學(xué)報》2017年05期

【摘要】：針對實際工程中噪聲難以避免和預(yù)測的問題,提出了魯棒性較強的加權(quán)模糊樹(W-FT)算法,采用基于局部異常因子(LOF)的加權(quán)最小二乘法代替最小二乘法學(xué)習(xí)模糊規(guī)則的后件參數(shù),通過2個典型的非線性例子驗證了該算法的有效性.應(yīng)用W-FT算法建立了電站鍋爐NOx排放特性模型,并與其他建模方法所建模型進行了對比.結(jié)果表明:所提出的W-FT算法能夠有效地辨識噪聲和異常值,具有較強的魯棒性,所建立的模型預(yù)測精度較高,泛化能力較強.
[Abstract]:Aiming at the problem that the noise is difficult to be avoided and predicted in practical engineering, a robust weighted fuzzy tree algorithm is proposed. The weighted least square method based on local anomaly factor (LOF) is used instead of the least square method to learn the parameters of fuzzy rules. The effectiveness of the algorithm is verified by two typical nonlinear examples. The model of NOx emission characteristics of utility boiler is established by using W-FT algorithm and compared with other modeling methods. The results show that the proposed W-FT algorithm can identify noise and outliers effectively and has strong robustness. The proposed model has high prediction accuracy and strong generalization ability.
【作者單位】： 華北電力大學(xué)新能源電力系統(tǒng)國家重點實驗室;華北電力大學(xué)工業(yè)過程測控新技術(shù)與系統(tǒng)北京市重點實驗室;
【基金】：國家重點基礎(chǔ)研究發(fā)展計劃(973計劃)資助項目(2012CB215203) 中央高�；究蒲袠I(yè)務(wù)費專項資金資助項目(2015MS33)
【分類號】：TB53;TM621.2
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本文編號：1969487