發(fā)布時(shí)間：2018-06-07 03:33

  本文選題：EIV系統(tǒng) + 合成模型　； 參考：《東華大學(xué)》2016年博士論文

【摘要】：隨著工業(yè)大數(shù)據(jù)時(shí)代的來臨,發(fā)展基于數(shù)據(jù)的建模方法顯得尤為重要�；诠I(yè)過程數(shù)據(jù)的建模問題,一個(gè)重要的挑戰(zhàn)是收集到的數(shù)據(jù)含有噪聲。傳統(tǒng)的基于工業(yè)過程輸入輸出數(shù)據(jù)的建模方法只考慮輸出測(cè)量數(shù)據(jù)存在噪聲,而忽略輸入測(cè)量數(shù)據(jù)存在噪聲,會(huì)導(dǎo)致已建立的模型存在較大的偏差甚至是無效的。變量誤差(EIV)系統(tǒng)是一種考慮可觀測(cè)或測(cè)量輸入輸出數(shù)據(jù)均含有誤差的系統(tǒng)。傳統(tǒng)的EIV系統(tǒng)并沒有考慮輸入生成動(dòng)態(tài)過程,本論文針對(duì)具有輸入生成動(dòng)態(tài)過程的EIV系統(tǒng)進(jìn)行研究,主要內(nèi)容如下:(1)針對(duì)具有線性輸入生成過程的線性EIV系統(tǒng)進(jìn)行研究,即線性EIV系統(tǒng)包括線性輸入生成過程和線性EIV過程。通過分析線性EIV系統(tǒng)中所有可觀測(cè)或測(cè)量變量之間的因果關(guān)系,提出合成線性EIV模型,合成線性EIV模型考慮了線性EIV系統(tǒng)中所有觀測(cè)或測(cè)量變量對(duì)被估計(jì)的輸入變量(實(shí)際輸入)產(chǎn)生的影響。進(jìn)一步,基于期望最大化算法估計(jì)合成線性EIV模型的參數(shù)。最后,采用數(shù)值仿真例子及混合水箱實(shí)驗(yàn)驗(yàn)證了提出方法的有效性。(2)考慮到工業(yè)過程本身具有非線性性,對(duì)非線性EIV系統(tǒng)進(jìn)行研究。其中非線性EIV系統(tǒng)是由非線性輸入生成過程和非線性EIV過程組成�？紤]到直接辨識(shí)非線性EIV系統(tǒng)存在較大的困難,采用多個(gè)線性EIV模型逼近非線性EIV模型的思想,對(duì)非線性模型進(jìn)行辨識(shí)。通過分析非線性EIV系統(tǒng)中所有觀測(cè)或測(cè)量變量之間的因果關(guān)系,提出了合成非線性EIV模型。在提出的合成非線性EIV模型中,設(shè)計(jì)了一種并行粒子濾波的策略用于估計(jì)每個(gè)線性EIV模型的實(shí)際輸入(被估計(jì)的狀態(tài)),即對(duì)多個(gè)線性EIV模型中的每一個(gè)線性模型均執(zhí)行一個(gè)粒子濾波,且與多個(gè)線性EIV模型相對(duì)應(yīng)的多個(gè)粒子濾波并行執(zhí)行。進(jìn)而,在最大似然的估計(jì)框架下,基于采集到的非線性EIV系統(tǒng)的輸入輸出數(shù)據(jù),通過期望最大化算法對(duì)提出的合成非線性EIV模型的參數(shù)進(jìn)行估計(jì)。數(shù)值模擬例子和實(shí)驗(yàn)例子驗(yàn)證了提出方法的有效性。(3)考慮到工業(yè)過程可觀測(cè)或測(cè)量數(shù)據(jù)通常存在異常觀測(cè)數(shù)據(jù),針對(duì)非線性EIV系統(tǒng),提出了一種魯棒辨識(shí)方法�？紤]到t分布可以通過調(diào)整自由度,有效地解決異常觀測(cè)數(shù)據(jù)對(duì)辨識(shí)過程造成的干擾。采用t分布對(duì)測(cè)量噪聲進(jìn)行建模,而不是傳統(tǒng)的高斯分布。進(jìn)一步,為了避免直接辨識(shí)非線性模型的復(fù)雜性,提出了采用多個(gè)魯棒線性模型近似非線性EIV系統(tǒng)。進(jìn)而,考慮指數(shù)函數(shù)作為權(quán)重函數(shù)加權(quán)合成多個(gè)魯棒線性局部模型的輸出得到非線性EIV系統(tǒng)的全局輸出。在極大似然估計(jì)的框架下,通過期望最大化算法同時(shí)估計(jì)得到每個(gè)魯棒線性局部模型的參數(shù)及權(quán)重函數(shù)的參數(shù)。最后,提出方法的有效性通過使用數(shù)字仿真例子及實(shí)驗(yàn)例子進(jìn)行了驗(yàn)證。(4)基于變分貝葉斯方法,針對(duì)存在異常觀測(cè)數(shù)據(jù)的非線性EIV系統(tǒng),提出了一種魯棒辨識(shí)方法�；谟^測(cè)或測(cè)量得到的非線性EIV系統(tǒng)的輸入輸出數(shù)據(jù),在貝葉斯框架下,通過使用變分貝葉斯期望最大化算法,解決了非線性EIV系統(tǒng)的參數(shù)估計(jì)問題,得到了非線性EIV系統(tǒng)模型參數(shù)的分布,而不是參數(shù)的點(diǎn)估計(jì)。并通過一個(gè)連續(xù)發(fā)酵過程作為仿真例子及一個(gè)三串聯(lián)水箱系統(tǒng)作為實(shí)驗(yàn)例子驗(yàn)證了提出方法的優(yōu)越性。最后,對(duì)本論文的研究?jī)?nèi)容進(jìn)行了總結(jié),并對(duì)下一步的研究工作和相關(guān)問題進(jìn)行了展望。
[Abstract]:With the coming of the era of industrial data, it is very important to develop the modeling method based on data. Based on the modeling of industrial process data, an important challenge is that the collected data contains noise. The traditional modeling method based on the input and output data of industrial process only considers the noise of the output data, and neglects the input. There is a noise in the measured data, which leads to the existence of large deviations or even ineffectiveness of the established model. The variable error (EIV) system is a system that takes into account the error of the input and output data which can be observed or measured. The traditional EIV system does not consider the dynamic process of the input generation, and this paper is aimed at the EI with the dynamic input generation process. The main contents of the V system are as follows: (1) a linear EIV system with linear input generating process is studied, that is, linear EIV system includes linear input generating process and linear EIV process. By analyzing the causality between all observable or measured variables in linear EIV system, a linear EIV model is proposed and a linear E is synthesized. The IV model considers the effects of all observation or measurement variables on the estimated input variables (actual input) in linear EIV systems. Further, based on the expectation maximization algorithm, the parameters of the synthetic linear EIV model are estimated. Finally, the effectiveness of the proposed method is verified by numerical simulation examples and mixed water tank experiments. (2) consideration of industry. The nonlinear process itself is nonlinear, and the nonlinear EIV system is studied. The nonlinear EIV system is composed of nonlinear input generating process and nonlinear EIV process. Considering the difficulties in identifying the nonlinear EIV system directly, multiple linear EIV models are used to approximate the non linear EIV model, and the nonlinear model is identified. By analyzing the causality between all observed or measured variables in the nonlinear EIV system, a synthetic nonlinear EIV model is proposed. In the proposed nonlinear EIV model, a parallel particle filtering strategy is designed to estimate the actual input (estimated state) of each linear EIV model, that is, multiple linear EIV models. Each linear model in the linear model performs a particle filter and performs parallel execution of multiple particle filters corresponding to multiple linear EIV models. Then, under the maximum likelihood estimation framework, the input and output data of the collected nonlinear EIV system are based on the expected maximization algorithm for the parameters of the proposed synthetic nonlinear EIV model. A line estimation. Numerical simulation examples and experimental examples show the effectiveness of the proposed method. (3) a robust identification method is proposed for nonlinear EIV systems considering the observable or measured data in industrial processes. A robust identification method is proposed for nonlinear EIV systems. Considering that the t distribution can be used to solve the abnormal observation data effectively by adjusting the degree of freedom. The disturbance caused by the recognition process is modeled by t distribution rather than the traditional Gauss distribution. Further, in order to avoid the complexity of nonlinear model identification directly, a multi robust linear model is proposed to approximate the nonlinear EIV system. Then, the exponential function is considered as weighting function to synthesize multiple robust linear bureaus. The output of the model is obtained from the global output of the nonlinear EIV system. Under the framework of the maximum likelihood estimation, the parameters and weights of each robust linear local model are estimated by the expected maximization algorithm. Finally, the effectiveness of the proposed method is verified by using digital simulation examples and experimental examples. (4) In the variational Bayesian method, a robust identification method is proposed for nonlinear EIV systems with abnormal observation data. Based on the observation or measurement of the input and output data of the nonlinear EIV system, the parameter estimation of the nonlinear EIV system is solved by using the variational Bayesian expectation maximization algorithm. The distribution of the parameters of the nonlinear EIV system model, not the point estimation of the parameters, is obtained. The superiority of the proposed method is verified by a continuous fermentation process as a simulation example and a three series water tank system as an experimental example. Finally, the research content of this paper is summarized, and the next step of the research work is also discussed. The related issues were prospected.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：N945.14

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1 郭凡;非線性變量誤差系統(tǒng)的辨識(shí)方法研究[D];東華大學(xué);2016年

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本文編號(hào)：1989566