發(fā)布時間：2018-03-04 12:28

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

【摘要】：近年來,信息物理系統(tǒng)受到了學術(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)化問題成為了一個研究熱點。信息物理系統(tǒng)自身具有的混成性、不確定性等特點,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)量嚴重依賴于設(shè)計人員的經(jīng)驗。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器學習的信息物理系統(tǒng)建模和優(yōu)化方法,主要工作包括:·提出了一種基于隨機混成自動機和模式本體的信息物理系統(tǒng)建模方法。該方法使用隨機混成自動機網(wǎng)絡(luò)對信息物理系統(tǒng)進行建模,抽取隨機混成自動機的基本概念,構(gòu)建上層模式本體。對上層模式本體進行實例化,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)計模型檢測工具UPPAAL-SMC對系統(tǒng)性能表現(xiàn)進行量化評估�！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,進行分類模型的訓練,建立分類模型,并給出了應(yīng)用分類模型進行資源調(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)配置多目標優(yōu)化方法。該方法首先選取待優(yōu)化參數(shù),進行參數(shù)實例的生成。選取部分實例的量化評估結(jié)果作為訓練集,建立參數(shù)實例與系統(tǒng)性能表現(xiàn)之間的關(guān)系模型,對預(yù)測集中參數(shù)實例的性能表現(xiàn)進行預(yù)測,從而快速得到大量參數(shù)配置在多個目標下的性能表現(xiàn),最終基于帕累托優(yōu)化方法得到多目標下系統(tǒng)的最優(yōu)化參數(shù)配置。最后,本文使用一個典型的信息物理系統(tǒng)——智能建筑系統(tǒng)對上述方法進行了詳細的實例驗證。實驗結(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,幫助系統(tǒng)設(shè)計人員高效建立較高質(zhì)量的系統(tǒng)模型;基于機器學習的信息物理系統(tǒng)優(yōu)化方法,可以幫助系統(tǒng)設(shè)計人員快速得到相對優(yōu)化的系統(tǒng)設(shè)計方案,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.
【學位授予單位】：華東師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP181;TP29

【參考文獻】

相關(guān)期刊論文 前6條

1 周興社;楊亞磊;楊剛;;信息-物理融合系統(tǒng)動態(tài)行為模型構(gòu)建方法[J];計算機學報;2014年06期

2 DAVID Alexandre;LARSEN Kim G.;MIKUIONIS Marius;SKOU Arne;;An evaluation framework for energy aware buildings using statistical model checking[J];Science China(Information Sciences);2012年12期

3 李仁發(fā);謝勇;李蕊;李浪;;信息-物理融合系統(tǒng)若干關(guān)鍵問題綜述[J];計算機研究與發(fā)展;2012年06期

4 黎作鵬;張?zhí)祚Y;張菁;;信息物理融合系統(tǒng)(CPS)研究綜述[J];計算機科學;2011年09期

5 公茂果;焦李成;楊咚咚;馬文萍;;進化多目標優(yōu)化算法研究[J];軟件學報;2009年02期

6 鄧志鴻,唐世渭,張銘,楊冬青,陳捷;Ontology研究綜述[J];北京大學學報(自然科學版);2002年05期

，

本文編號：1565652