本文選題：信息物理系統(tǒng)　切入點(diǎn)：建模與優(yōu)化　出處：《華東師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,信息物理系統(tǒng)受到了學(xué)術(shù)界和工業(yè)界越來越多的關(guān)注,其復(fù)雜度使其建模與優(yōu)化問題成為了一個(gè)研究熱點(diǎn)。信息物理系統(tǒng)自身具有的混成性、不確定性等特點(diǎn),給建模工作帶來了一定的困難。而且,信息物理系統(tǒng)的領(lǐng)域相關(guān)性,使得其建模的質(zhì)量嚴(yán)重依賴于設(shè)計(jì)人員的經(jīng)驗(yàn)。另外,信息物理系統(tǒng)所處環(huán)境中可控和不可控因素并存,如何針對這些因素進(jìn)行系統(tǒng)優(yōu)化也困擾著研究人員。針對這些問題,本文提出了基于模式本體和機(jī)器學(xué)習(xí)的信息物理系統(tǒng)建模和優(yōu)化方法,主要工作包括:·提出了一種基于隨機(jī)混成自動(dòng)機(jī)和模式本體的信息物理系統(tǒng)建模方法。該方法使用隨機(jī)混成自動(dòng)機(jī)網(wǎng)絡(luò)對信息物理系統(tǒng)進(jìn)行建模,抽取隨機(jī)混成自動(dòng)機(jī)的基本概念,構(gòu)建上層模式本體。對上層模式本體進(jìn)行實(shí)例化,構(gòu)建領(lǐng)域模式本體。給出了基于模式本體的建模方法,并在此基礎(chǔ)上使用統(tǒng)計(jì)模型檢測工具UPPAAL-SMC對系統(tǒng)性能表現(xiàn)進(jìn)行量化評估�！め槍π畔⑽锢硐到y(tǒng)所處物理世界中的不可控因素,提出了一種基于支持向量分類的資源調(diào)度策略選擇方法。該方法以多種用戶行為下各種調(diào)度策略的性能表現(xiàn)評估結(jié)果為原始數(shù)據(jù),經(jīng)過數(shù)據(jù)預(yù)處理后,進(jìn)行分類模型的訓(xùn)練,建立分類模型,并給出了應(yīng)用分類模型進(jìn)行資源調(diào)度策略選擇的方法�！め槍π畔⑽锢硐到y(tǒng)物理世界中的可控因素,提出了一種基于支持向量回歸和帕累托優(yōu)化的參數(shù)配置多目標(biāo)優(yōu)化方法。該方法首先選取待優(yōu)化參數(shù),進(jìn)行參數(shù)實(shí)例的生成。選取部分實(shí)例的量化評估結(jié)果作為訓(xùn)練集,建立參數(shù)實(shí)例與系統(tǒng)性能表現(xiàn)之間的關(guān)系模型,對預(yù)測集中參數(shù)實(shí)例的性能表現(xiàn)進(jìn)行預(yù)測,從而快速得到大量參數(shù)配置在多個(gè)目標(biāo)下的性能表現(xiàn),最終基于帕累托優(yōu)化方法得到多目標(biāo)下系統(tǒng)的最優(yōu)化參數(shù)配置。最后,本文使用一個(gè)典型的信息物理系統(tǒng)——智能建筑系統(tǒng)對上述方法進(jìn)行了詳細(xì)的實(shí)例驗(yàn)證。實(shí)驗(yàn)結(jié)果表明,本文提出的基于模式本體的信息物理系統(tǒng)建模方法可以重用領(lǐng)域知識(shí),幫助系統(tǒng)設(shè)計(jì)人員高效建立較高質(zhì)量的系統(tǒng)模型;基于機(jī)器學(xué)習(xí)的信息物理系統(tǒng)優(yōu)化方法,可以幫助系統(tǒng)設(shè)計(jì)人員快速得到相對優(yōu)化的系統(tǒng)設(shè)計(jì)方案,滿足相應(yīng)的非功能需求。
[Abstract]:In recent years, more and more attention has been paid to information physics systems in academia and industry. The complexity of information physics systems makes modeling and optimization problems become a research hotspot. Information physics systems have their own characteristics such as mixing, uncertainty and so on. It brings some difficulties to the modeling work. Moreover, the domain relativity of the information physics system makes the modeling quality depend heavily on the designer's experience. In addition, the controllable and uncontrollable factors coexist in the environment of the information physics system. How to optimize the system based on these factors also puzzles the researchers. Aiming at these problems, a method of modeling and optimizing information physics system based on pattern ontology and machine learning is proposed in this paper. The main work includes: 路A modeling method of information physics system based on random hybrid automata and pattern ontology is proposed, which uses random hybrid automaton network to model information physics system. The basic concept of random hybrid automata is extracted, the upper pattern ontology is constructed, the upper pattern ontology is instantiated, the domain pattern ontology is constructed, and the modeling method based on pattern ontology is presented. On this basis, the statistical model detection tool UPPAAL-SMC is used to quantitatively evaluate the performance of the system. 路aiming at the uncontrollable factors in the physical world of the information physics system, This paper presents a resource scheduling policy selection method based on support vector classification, which uses the performance evaluation results of various scheduling strategies under various user behaviors as raw data. After data preprocessing, the classification model is trained. The classification model is established, and the method of resource scheduling policy selection by applying the classification model is given. A multi-objective optimization method for parameter configuration based on support vector regression and Pareto optimization is proposed. The relationship model between parameter instances and system performance is established to predict the performance of parameter instances in the prediction set, and the performance performance of a large number of parameter configurations under multiple targets is obtained quickly. Finally, based on the Pareto optimization method, the optimal parameter configuration of the multi-objective system is obtained. Finally, a typical information physical system-Intelligent Building system is used to verify the above method in detail. The experimental results show that, The information physics system modeling method based on pattern ontology can reuse domain knowledge and help system designers to build high quality system model efficiently. It can help the system designers to quickly obtain the relative optimization of the system design scheme to meet the corresponding non-functional requirements.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP181;TP29

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