發(fā)布時(shí)間：2018-01-28 04:57

  本文關(guān)鍵詞： 總磷 軟測(cè)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(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著社會(huì)的進(jìn)步與經(jīng)濟(jì)的蓬勃發(fā)展,環(huán)境污染和生態(tài)惡化等問題愈發(fā)嚴(yán)峻,隨之水污染問題越發(fā)凸顯。加強(qiáng)水質(zhì)監(jiān)測(cè),不僅關(guān)乎國(guó)民經(jīng)濟(jì)的發(fā)展,對(duì)于人們的身體健康也具有現(xiàn)實(shí)意義。水體富營(yíng)養(yǎng)化的機(jī)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(biāo)污水總磷(Total Phosphorus,TP)難以進(jìn)行在線監(jiān)測(cè)。水質(zhì)監(jiān)測(cè)是水體評(píng)價(jià)的前提,對(duì)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(cè)量模型應(yīng)用廣泛,能夠準(zhǔn)確建立復(fù)雜系統(tǒng)的模型。針對(duì)污水處理系統(tǒng)具有復(fù)雜動(dòng)態(tài)特性多噪聲、非線性的時(shí)變系統(tǒng),建立基于回聲狀態(tài)網(wǎng)絡(luò)的出水總磷軟測(cè)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(dòng)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(dòng)態(tài)變化過程,實(shí)現(xiàn)對(duì)于出水水質(zhì)參數(shù)TP的在線預(yù)測(cè)。本文主要的研究工作包括以下幾點(diǎn):1.提出出水TP軟測(cè)量模型設(shè)計(jì)。論文中針對(duì)TP軟測(cè)量模型的設(shè)計(jì)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(duì)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(cè)量模型。本文詳細(xì)出水TP軟測(cè)量模型的建立過程,并證實(shí)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(duì)回聲狀態(tài)網(wǎng)絡(luò)在訓(xùn)練過程中使用偽逆算法對(duì)輸出權(quán)重進(jìn)行訓(xùn)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(cè)精度。依據(jù)回聲狀態(tài)網(wǎng)絡(luò)結(jié)構(gòu)特點(diǎn)在標(biāo)準(zhǔn)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(jìn)粒子群算法。通過對(duì)于標(biāo)準(zhǔn)測(cè)試函數(shù)進(jìn)行測(cè)試,驗(yàn)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(jìn)回聲狀態(tài)網(wǎng)絡(luò)TP軟測(cè)量模型設(shè)計(jì)。結(jié)合出水TP的特點(diǎn)和軟測(cè)量技術(shù)的研究,提出基于改進(jìn)回聲狀態(tài)網(wǎng)絡(luò)建立出水TP的軟測(cè)量模型。通過Mackey-Glass混沌時(shí)間序列預(yù)測(cè)的預(yù)測(cè),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(cè)量模型提供基礎(chǔ)。結(jié)合之前的研究,基于改進(jìn)回聲狀態(tài)網(wǎng)絡(luò)軟測(cè)量模型應(yīng)用到出水TP預(yù)測(cè),證明所設(shè)計(jì)的出水TP軟測(cè)量模型設(shè)計(jì)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(xué)位授予單位】：北京工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP183;X832
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本文編號(hào)：1469828