本文選題：磨礦分級　切入點：Takagi-Sugeno模型　出處：《大連理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：磨礦工業(yè)過程復(fù)雜,生產(chǎn)線上的控制變量較多,變量間的強非線性動態(tài)關(guān)系致使磨礦模型較難用精確的數(shù)學(xué)模型來描述,一般的控制方法難以對其進(jìn)行控制,多數(shù)磨礦過程的控制一般需要操作員依據(jù)自身經(jīng)驗對控制變量進(jìn)行實時調(diào)控,但由于操作員的主觀經(jīng)驗限制、工況的復(fù)雜和邊界條件的多變性,人工控制往往難以達(dá)到預(yù)期的生產(chǎn)指標(biāo)。對磨礦過程的有效建模,有助于磨礦過程自動化生產(chǎn)的實現(xiàn),可以避免人工控制的主觀性帶來的誤操作,提高礦產(chǎn)資源的利用率,降低選礦廠的生產(chǎn)成本,提高產(chǎn)量。以選礦工業(yè)過程中的磨礦系統(tǒng)這一復(fù)雜過程為背景,針對磨礦系統(tǒng)建模問題,本文提出了一種基于子集融合與規(guī)則簡約的模糊建模方法。首先根據(jù)工業(yè)數(shù)據(jù),采用一種數(shù)據(jù)驅(qū)動的方法建立初始的磨礦過程Takagi-Sugeno模型,然后針對該模型中隸屬度函數(shù)過擬合問題,提出一種基于模糊C均值聚類(Fuzzy C-means Clustering, FCM)的方法,對初始規(guī)則庫中同一變量下的隸屬度函數(shù)參數(shù)進(jìn)行聚類,得到對不同工況具有代表性的融合后的隸屬度函數(shù),來降低建模過程過擬合的影響。最后,針對模糊集融合后規(guī)則庫中出現(xiàn)的規(guī)則冗余問題,本文定義了冗余規(guī)則相似度,并根據(jù)該相似度,對前件相同的冗余規(guī)則進(jìn)行約簡,消除冗余,形成最終的泛化能力較強的離線模糊規(guī)則庫。為驗證本文方法的有效性,以另外幾種模糊建模方法作為對比,分別采用經(jīng)典數(shù)據(jù)與國內(nèi)某選礦廠的實際工業(yè)數(shù)據(jù)進(jìn)行實驗驗證。兩組實驗分別從模型的精度和結(jié)構(gòu)方面對多種建模方法進(jìn)行了對比,結(jié)果表明,本文方法較其他幾種方法在精度和結(jié)構(gòu)方面均體現(xiàn)出了一定程度的優(yōu)勢。最后,以本文建模方法為后臺算法,開發(fā)了磨礦智能控制系統(tǒng),對實際磨礦工業(yè)過程實現(xiàn)自動化控制。
[Abstract]:The process of grinding industry is complex, the control variables in production line are many, and the strong nonlinear dynamic relationship between variables makes it difficult to describe the grinding model by precise mathematical model, and it is difficult to control it by general control methods. The control of most grinding processes usually requires the operator to adjust the control variables in real time according to his own experience. However, due to the limitation of the operator's subjective experience, the operating conditions are complex and the boundary conditions vary. The effective modeling of grinding process is helpful to the realization of automatic production of grinding process, which can avoid the misoperation caused by subjectivity of manual control and improve the utilization ratio of mineral resources. Based on the complex process of grinding system in the process of dressing industry, aiming at the modeling problem of grinding system, the production cost of concentrator is reduced and the output is increased. In this paper, a fuzzy modeling method based on subset fusion and rule reduction is proposed. Firstly, according to industrial data, an initial Takagi-Sugeno model of grinding process is established by using a data-driven method. Then, a fuzzy C-means clustering method based on fuzzy C-means clustering (FCM) is proposed for the over-fitting of membership function in this model. The parameters of membership function under the same variable in the initial rule base are clustered. In order to reduce the influence of over-fitting in modeling process, the membership function after fusion is obtained for different working conditions. Finally, the similarity of redundant rules is defined in this paper, aiming at the problem of rule redundancy in the rule base after fuzzy set fusion. According to the similarity, the same redundancy rules are reduced to eliminate redundancy and form an off-line fuzzy rule base with strong generalization ability. In order to verify the effectiveness of this method, several other fuzzy modeling methods are compared. The classical data and the actual industrial data of a domestic concentrator are used to verify the experiment. The two groups of experiments are compared with each other in terms of model precision and structure. The results show that, Compared with other methods, this method has some advantages in precision and structure. Finally, an intelligent grinding control system is developed based on the method of modeling in this paper, which can realize the automatic control of the actual grinding industry process.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TD921.4;TP18

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本文編號：1615940