本文關(guān)鍵詞：粗糙集—決策樹(shù)在故障診斷中的應(yīng)用研究　出處：《東北大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

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【摘要】：目前,把多種數(shù)據(jù)挖掘技術(shù)相結(jié)合從而實(shí)現(xiàn)對(duì)故障問(wèn)題的診斷已經(jīng)成為未來(lái)故障診斷研究的發(fā)展趨勢(shì)。決策樹(shù)模型憑借其可讀性強(qiáng)、分類速度快等優(yōu)點(diǎn)在故障診斷領(lǐng)域發(fā)揮著不可替代的作用,然而,訓(xùn)練數(shù)據(jù)集中的噪聲數(shù)據(jù)以及在建模過(guò)程中存在的過(guò)擬合問(wèn)題嚴(yán)重制約著決策樹(shù)模型的診斷效率。粗糙集相關(guān)理論則能在保持?jǐn)?shù)據(jù)分類能力不變的前提下,提高模型對(duì)噪聲數(shù)據(jù)的容忍程度,從而擴(kuò)大對(duì)數(shù)據(jù)的應(yīng)用范圍。故二者的結(jié)合成為故障診斷領(lǐng)域新的研究方向。本文以構(gòu)建粗糙集-決策樹(shù)模型以及實(shí)現(xiàn)對(duì)故障數(shù)據(jù)的診斷為目的,主要做了以下幾方面的工作：首先,對(duì)粗糙集的經(jīng)典理論和決策樹(shù)算法的進(jìn)行了梳理,分別分析了這兩種方法與其他數(shù)據(jù)挖掘手段結(jié)合下的應(yīng)用現(xiàn)狀。其次,在比較3種決策樹(shù)算法后,提出用C4.5算法替代前人提出的粗糙集-決策樹(shù)算法中的ID3算法,從而使模型能夠克服因?qū)傩匀≈挡煌瑤?lái)的誤差。再次,針對(duì)已有的粗糙集-決策樹(shù)模型不能很好的克服噪聲數(shù)據(jù)這一現(xiàn)象,引入粗糙集理論中的變精度概念,將其應(yīng)用到?jīng)Q策樹(shù)初始變量的選擇過(guò)程中,從而實(shí)現(xiàn)改進(jìn)后的決策樹(shù)模型能夠克服一定程度下的噪聲數(shù)據(jù)。最后,把改進(jìn)后的粗糙集—決策樹(shù)模型應(yīng)用到故障診斷數(shù)據(jù)中,并與C4.5決策樹(shù)模型進(jìn)行有關(guān)比較,驗(yàn)證了前者相較于后者,決策樹(shù)規(guī)模更小,預(yù)測(cè)能力更好,生成的規(guī)則更加豐富。
[Abstract]:At present, various data mining technology combined to realize the fault diagnosis problem has become the future development trend of fault diagnosis. The decision tree model with its readability, classification speed and other advantages in the field of fault diagnosis plays an irreplaceable role, however, the noise data and existing in the training data set in the process of modeling the over fitting problem restricts the efficiency of diagnosis decision tree model. Rough set theory can while keeping the classification ability of data at the same time, improve the degree of tolerance to noise data model, so as to expand the scope of application of data binding. So the two is becoming a new research direction in the field of fault diagnosis. This paper focuses on the construction of rough set and decision tree model and realize the fault diagnosis data for the purpose, mainly do the following work: firstly, the classical rough sets theory Theory and decision tree algorithm are summarized, analyzed the two methods and other methods of data mining combined with the application situation. Secondly, after comparing 3 kinds of decision tree algorithms, proposed previously proposed rough set with C4.5 algorithm of decision tree algorithm ID3 algorithm, which can overcome the model because the error caused by the different attribute values. Thirdly, according to the existing rough set and decision tree model can well overcome the phenomenon of noise data, using rough set of variable precision concept in theory, which is used to select the initial decision tree variables, so as to realize the improved decision tree model can overcome the noise the data under a certain degree. Finally, the improved rough set and decision tree model is applied to the fault diagnosis data, and the comparison with the C4.5 decision tree model, verified the former compared with the latter, the decision tree size Smaller, better predictive, and more abundant rules.

【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：C934

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本文編號(hào)：1397060