發(fā)布時間：2018-01-28 04:57

  本文關(guān)鍵詞： 總磷 軟測量模型 回聲狀態(tài)網(wǎng)絡(luò) 自適應(yīng)變異粒子群算法　出處：《北京工業(yè)大學》2015年碩士論文　論文類型：學位論文

【摘要】：隨著社會的進步與經(jīng)濟的蓬勃發(fā)展,環(huán)境污染和生態(tài)惡化等問題愈發(fā)嚴峻,隨之水污染問題越發(fā)凸顯。加強水質(zhì)監(jiān)測,不僅關(guān)乎國民經(jīng)濟的發(fā)展,對于人們的身體健康也具有現(xiàn)實意義。水體富營養(yǎng)化的機理過程復(fù)雜,影響因素眾多,難以得到有效控制以致現(xiàn)階段發(fā)生率高,而其關(guān)鍵水質(zhì)參數(shù)指標污水總磷(Total Phosphorus,TP)難以進行在線監(jiān)測。水質(zhì)監(jiān)測是水體評價的前提,對于污染防治起到預(yù)警作用。近年來,基于人工神經(jīng)網(wǎng)絡(luò)的軟測量模型應(yīng)用廣泛,能夠準確建立復(fù)雜系統(tǒng)的模型。針對污水處理系統(tǒng)具有復(fù)雜動態(tài)特性多噪聲、非線性的時變系統(tǒng),建立基于回聲狀態(tài)網(wǎng)絡(luò)的出水總磷軟測量模型。由于遞歸神經(jīng)網(wǎng)絡(luò)能夠以任意精度逼近非線性函數(shù)以及良好的動態(tài)信息處理能力,該模型能夠有效模擬污水處理系統(tǒng)的非線性動態(tài)變化過程,實現(xiàn)對于出水水質(zhì)參數(shù)TP的在線預(yù)測。本文主要的研究工作包括以下幾點:1.提出出水TP軟測量模型設(shè)計。論文中針對TP軟測量模型的設(shè)計概括為以下步驟,首先數(shù)據(jù)的采集以及樣本數(shù)據(jù)的預(yù)處理,然后通過主成分分析法對于TP相關(guān)相關(guān)輔助變量精選,最后建立神經(jīng)網(wǎng)絡(luò)軟測量模型。本文詳細出水TP軟測量模型的建立過程,并證實其有效性。2.提出一種自適應(yīng)變異粒子群算法。論文中針對回聲狀態(tài)網(wǎng)絡(luò)在訓練過程中使用偽逆算法對輸出權(quán)重進行訓練,難以保證回聲網(wǎng)絡(luò)的穩(wěn)定性,影響網(wǎng)絡(luò)的穩(wěn)定性和預(yù)測精度。依據(jù)回聲狀態(tài)網(wǎng)絡(luò)結(jié)構(gòu)特點在標準粒子群算法的基礎(chǔ)上,采用自適應(yīng)變異策略,提出一種改進粒子群算法。通過對于標準測試函數(shù)進行測試,驗證該算法具有搜索速度快,能夠有效避免陷入局部最優(yōu)中。3.基于改進回聲狀態(tài)網(wǎng)絡(luò)TP軟測量模型設(shè)計。結(jié)合出水TP的特點和軟測量技術(shù)的研究,提出基于改進回聲狀態(tài)網(wǎng)絡(luò)建立出水TP的軟測量模型。通過Mackey-Glass混沌時間序列預(yù)測的預(yù)測,有效證明其應(yīng)用在非線性系統(tǒng)的有效性,為接下來的TP軟測量模型提供基礎(chǔ)。結(jié)合之前的研究,基于改進回聲狀態(tài)網(wǎng)絡(luò)軟測量模型應(yīng)用到出水TP預(yù)測,證明所設(shè)計的出水TP軟測量模型設(shè)計的有效性。
[Abstract]:With the development of society and economy, the problems of environmental pollution and ecological deterioration become more and more serious, and the problem of water pollution becomes more prominent. Strengthening water quality monitoring is not only related to the development of national economy. The mechanism of eutrophication of water body is complex and the influence factors are many, so it is difficult to get effective control and the incidence of eutrophication is high at the present stage. However, it is difficult to carry out on-line monitoring of the key water quality parameters, total total phosphorus phosphate (TP), and water quality monitoring is the premise of water body evaluation. In recent years, the soft sensor model based on artificial neural network is widely used, which can accurately establish the model of complex system. The sewage treatment system has complex dynamic characteristics and many noises. The soft sensing model of total phosphorus in effluent based on echo state network is established for nonlinear time-varying system. The recurrent neural network can approach nonlinear function with arbitrary accuracy and has good dynamic information processing ability. The model can effectively simulate the nonlinear dynamic process of sewage treatment system. On-line prediction of effluent quality parameters TP is realized. The main research work in this paper includes the following points:. 1. The design of effluent TP soft sensor model is proposed. The design of TP soft sensor model is summarized as follows. First, the collection of data and sample data preprocessing, and then through the principal component analysis of TP related auxiliary variables selected. Finally, the soft sensing model of neural network is established, and the process of setting up the soft sensor model of effluent TP is discussed in detail in this paper. And verify its validity. 2. An adaptive mutation particle swarm optimization algorithm is proposed. In this paper, pseudo-inverse algorithm is used to train the output weight in the training process of echo state network. It is difficult to ensure the stability of echo network and affect the stability and prediction accuracy of the network. Based on the characteristics of echo state network structure and the standard particle swarm optimization algorithm, adaptive mutation strategy is adopted. An improved particle swarm optimization (PSO) algorithm is proposed, which is proved to be fast by testing the standard test function. It can effectively avoid falling into local optimum. 3. The design of TP soft sensor model based on improved echo state network, combined with the characteristics of effluent TP and the research of soft sensing technology. A soft sensing model of effluent TP based on improved echo state network is proposed. The validity of its application in nonlinear systems is proved by the prediction of Mackey-Glass chaotic time series. Based on the previous research, the improved echo state network soft sensor model is applied to TP prediction of effluent. It is proved that the designed effluent TP soft sensor model is effective.
【學位授予單位】：北京工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP183;X832
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本文編號：1469828