本文關(guān)鍵詞： 統(tǒng)計(jì)建模 小波 核方法 支持向量機(jī) 谷氨酸 發(fā)酵 廣義可加模型　出處：《江南大學(xué)》2011年博士論文　論文類型：學(xué)位論文

【摘要】：在當(dāng)今信息時(shí)代,各種統(tǒng)計(jì)方法層出不窮,統(tǒng)計(jì)知識(shí)得到越來(lái)越多的應(yīng)用。例如,統(tǒng)計(jì)的多尺度建模無(wú)論是在理論統(tǒng)計(jì)學(xué)還是在應(yīng)用統(tǒng)計(jì)學(xué)中現(xiàn)都已成為熱門課題,這無(wú)論對(duì)統(tǒng)計(jì)方法還是其在各個(gè)應(yīng)用科學(xué)領(lǐng)域的發(fā)展都起著沖擊作用;基于核的學(xué)習(xí)方法引起了數(shù)據(jù)分析領(lǐng)域的一場(chǎng)革命;廣義可加模型高度的靈活性,為有效揭示數(shù)據(jù)間所隱含的各種關(guān)系提供了一種有效的方法。在化工領(lǐng)域,一個(gè)有效的過(guò)程模型的建立,對(duì)研究如何科學(xué)規(guī)劃生產(chǎn)工藝,進(jìn)而實(shí)現(xiàn)生產(chǎn)過(guò)程的優(yōu)化意義重大。 針對(duì)常規(guī)預(yù)測(cè)函數(shù)模型存在未將預(yù)測(cè)時(shí)域的優(yōu)化從總體上考慮的不足,在統(tǒng)計(jì)的多尺度建模方面研究后,基于小波多尺度的特性而提出了基于小波基函數(shù)和Hammerstein模型的預(yù)測(cè)函數(shù)模型,其內(nèi)部模型參數(shù)可以通過(guò)不斷辨識(shí),自適應(yīng)的進(jìn)行校正。利用小波的緊支局部性和多尺度分析特性,既保證了整體誤差性能的優(yōu)化,又突出了重要擬合點(diǎn)的逼近要求,并實(shí)現(xiàn)了優(yōu)化變量的集結(jié)。理論分析和仿真應(yīng)用表明,該方法有更好的跟蹤性和抗模型失配性能。 (1)針對(duì)如何提高核方法的建模精度的同時(shí)還要兼顧建模速度的問(wèn)題,通過(guò)核方法研究,結(jié)合小波分析的理論,提出了小波融合核的建模方法。該方法具有小波多分辨率分析和核方法對(duì)輸入維數(shù)不敏感的特點(diǎn),理論上在保證建模精度的前提下,有更快的建模速度。在此基礎(chǔ)上,分別通過(guò)一維函數(shù)和化工生產(chǎn)數(shù)據(jù)進(jìn)行了仿真研究,仿真結(jié)果也驗(yàn)證了算法的有效性。(2)由可分Hilbert空間與L~2 ( R )的等價(jià)性,利用內(nèi)積同構(gòu)的線性算子,可以把L~2 ( R )中子空間的小波尺度函數(shù)折算為Hilbert空間中子空間的小波尺度函數(shù)。基于支持向量機(jī)核函數(shù)的條件和小波多分辨率理論,在Hilbert空間構(gòu)造出Morlet小波核函數(shù)。通過(guò)仿真實(shí)驗(yàn),與傳統(tǒng)的RBF核函數(shù)相比較,該尺度再生核函數(shù)具有更高的精度和更好的泛化能力。(3)在應(yīng)用融合核支持向量機(jī)建模以提高模型的泛化能力和精度時(shí),為避免在進(jìn)行核融合時(shí),支持向量機(jī)稀疏性的缺失,提出了將數(shù)據(jù)映射到稀疏特征空間進(jìn)行研究。通過(guò)仿真研究表明,所建模型在保證稀疏性的前提下,能提高建模精度,從而驗(yàn)證了算法的有效性,有良好的應(yīng)用意義。 針對(duì)谷氨酸發(fā)酵過(guò)程復(fù)雜,如何解決難以建立有效的模型來(lái)指導(dǎo)生產(chǎn)過(guò)程優(yōu)化的現(xiàn)狀的研究中,發(fā)現(xiàn)廣義可加模型(GAM)能為谷氨酸的發(fā)酵過(guò)程提供行之有效的建模方法。利用該方法可以方便的分析不同的建模變量對(duì)谷氨酸產(chǎn)量的影響并從中得出與谷氨酸產(chǎn)量間的關(guān)系。研究中,基于15批次發(fā)酵實(shí)驗(yàn)數(shù)據(jù),通過(guò)對(duì)不同影響因素的分析,最終選擇三個(gè)顯著影響因素(時(shí)間T、溶氧DO和氧攝取率OUR)來(lái)構(gòu)建GAM模型,這一模型可以對(duì)谷氨酸的發(fā)酵過(guò)程解釋97%。該模型的構(gòu)建成功,為研究發(fā)酵過(guò)程中不同因素對(duì)谷氨酸產(chǎn)量的影響提供了基礎(chǔ)。該模型不僅為根據(jù)在線數(shù)據(jù)預(yù)測(cè)谷氨酸產(chǎn)量提供了可行有效的方法,而且為發(fā)酵過(guò)程中在線故障診斷提供了新思路。在谷氨酸發(fā)酵過(guò)程故障診斷的方法研究中,提出了基于GAMs和Bootstrap方法的故障診斷方法。該方法能只依靠顯著觀測(cè)變量就可對(duì)發(fā)酵過(guò)程的狀態(tài)是否正常做出判斷,并能初步給出故障源相關(guān)的觀測(cè)變量。該方法只有很少的參數(shù)需要確定和調(diào)整,在發(fā)酵過(guò)程中,一方面能及時(shí)的對(duì)故障狀態(tài)進(jìn)行報(bào)告,另一方面為排除故障源提供必要的參考信息,從而為發(fā)酵過(guò)程的正常運(yùn)行提供了可靠的保障。 總之,隨著計(jì)算機(jī)技術(shù)的快速普及和廣泛發(fā)展,面對(duì)著數(shù)據(jù)和信息爆炸的挑戰(zhàn),為迅速有效地將數(shù)據(jù)提升為信息、知識(shí)和智能,統(tǒng)計(jì)建模方法在工業(yè)領(lǐng)域的研究意義重大。
[Abstract]:In today's information age, various statistical methods emerge in an endless stream of statistical knowledge, get more and more applications. For example, multiscale modeling statistics both in theoretical statistics or applied statistics have now become a hot topic, both the statistical methods and the application in various fields of science development plays a role in learning impact; method based on the kernel caused a revolution in the field of data analysis; generalized additive models can be highly flexibility, provides an effective method for revealing the implicit various relationships among data. In the chemical field, establish an effective process model, to study how to scientifically plan the production process, so as to realize the the optimization of production process.
Aiming at the shortage will not consider optimizing the overall prediction horizon from the existence of the conventional predictive function model, research in statistical aspects of multiscale modeling, feature based on wavelet multi-scale and proposes the prediction function model of wavelet function and Hammerstein model based on the internal model parameters can be through continuous identification, adaptive correction. Analysis of the characteristics of using compactly supported wavelets and multiscale, both to ensure the optimization of overall error performance, and some important points fitting, and the optimal parameters. The theoretical analysis and simulation show a better tracking performance and anti model mismatch performance of this method.
(1) in order to improve the modeling accuracy of kernel methods but also the modeling speed, by the nuclear method, combined with wavelet analysis theory, put forward the modeling method of wavelet fusion kernel. This method has the features of wavelet multiresolution analysis and kernel method is not sensitive to the input dimension theory, under the premise of ensuring modeling the accuracy of modeling, faster. On this basis, we have studied the one-dimensional function and chemical production data, the simulation results verify the validity of the algorithm. (2) when the Hilbert space and L~ 2 (R) of equivalence, using linear operator product isomorphism, can L~2 (R) wavelet scale function and wavelet scale function conversion neutron space as a subspace in Hilbert space. Conditions of the support vector kernel function and wavelet multi-resolution theory based on Hilbert space structure Morlet The nuclear wave function. Through the simulation experiment, compared with the traditional RBF kernel function, the scaling reproducing kernel function has higher accuracy and better generalization ability. (3) in the application of nuclear fusion support vector machine modeling to improve the generalization ability and the precision of the model, in order to avoid nuclear fusion timely, lack of support the sparse vector machine, the research data is mapped to the sparse feature space. The simulation results show that the model under the premise of guaranteeing sparsity, can improve the modeling accuracy, which verifies the validity of the algorithm, has good application significance.
In view of the glutamic acid fermentation process is complex, research how to solve it is difficult to establish an effective model to guide the status of production process optimization, find the generalized additive model (GAM) can provide effective modeling method for the fermentation process of glutamic acid. The method can affect convenient modeling and analysis of the different variables to the yield of glutamic acid and from and that the yield of glutamic acid. Among the studies, 15 batch fermentation based on experimental data, through the analysis of the influence of different factors, the final choice of the three significant factors (T, DO and dissolved oxygen uptake rate OUR) to construct the GAM model, this model can explain the fermentation process of glutamic acid was successfully constructed the 97%. model the influence of different factors to provide a basis for research on the fermentation process of glutamic acid production. The model not only for predicting the yield of glutamic acid according to online data provides a feasible The effective method, and provides a new idea for online fault diagnosis in the process of fermentation. In the research of fault diagnosis method of glutamic acid fermentation process, this paper presents a fault diagnosis method based on GAMs and Bootstrap method. This method can only rely on significant variables can be the state of the fermentation process is normal judgment, and observation variables the preliminary fault source. This method gives only a few parameters need to be determined and adjusted in the fermentation process, on the one hand to the failure state of the report, on the other hand, to provide the necessary information for troubleshooting source, so as to provide a reliable guarantee for the normal running of the fermentation process.
In short, with the rapid popularization and extensive development of computer technology, facing the challenge of data and information explosion, the research of statistical modeling is of great significance in the industrial field for rapidly and effectively upgrading data to information, knowledge and intelligence.

【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2011
【分類號(hào)】：C81

【引證文獻(xiàn)】

相關(guān)博士學(xué)位論文 前2條

1 孟憲勇;圖模型基礎(chǔ)理論研究[D];東北師范大學(xué);2012年

2 陳進(jìn)東;基于模糊在線支持向量回歸的建模與預(yù)測(cè)控制研究[D];江南大學(xué);2013年

相關(guān)碩士學(xué)位論文 前1條

1 韓順成;在線支持向量機(jī)在發(fā)酵過(guò)程建模中的應(yīng)用[D];江南大學(xué);2013年

，

本文編號(hào)：1474885