請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/1331完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 詹魁元(Kuei-Yuan Chan) | |
| dc.contributor.author | Min-Hsien Lee | en |
| dc.contributor.author | 李旻憲 | zh_TW |
| dc.date.accessioned | 2021-05-12T09:36:33Z | - |
| dc.date.available | 2018-08-21 | |
| dc.date.available | 2021-05-12T09:36:33Z | - |
| dc.date.copyright | 2018-08-21 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-18 | |
| dc.identifier.citation | [1] Andrew H. Briggs, Milton C. Weinstein, Elisabeth A. L. Fenwick, Jonathan Karnon, Mark J. Sculpher, and A. David Paltiel. Model parameter estimation and uncertainty: A report of the ispor-smdm modeling good research practices task force-6. Value in Health, 15(6):835–842, September 2012.
[2] Giulio Reina, Matilde Paiano, and Jose-Luis Blanco-Claraco. Vehicle parameter estimation using a model-based estimator. Mechanical Systems and Signal Processing, 87:227 – 241, 2017. Signal Processing and Control challenges for Smart Vehicles. [3] J. C. Jensen, D. H. Chang, and E. A. Lee. A model-based design methodology for cyber-physical systems. In 2011 7th International Wireless Communications and Mobile Computing Conference, pages 1666–1671, July 2011. [4] Richard C. Aster, Brian Borchers, and Clifford H. Thurber. Chapter one - introduction. In Richard C. Aster, Brian Borchers, and Clifford H. Thurber, editors, Parameter Estimation and Inverse Problems (Second Edition), pages 1 – 23. Academic Press, Boston, second edition edition, 2013. [5] J.L. Crassidis and J.L. Junkins. Optimal Estimation of Dynamic Systems. Chapman & Hall/CRC Applied Mathematics & Nonlinear Science. Taylor & Francis, 2004. [6] F. Liu, M. Bayarri, and J. Berger. Modularization in bayesian analysis, with emphasis on analysis of computer models. Bayesian Analysis, 4(1):119–150, 2009. [7] P.L. Green, E.J. Cross, and K. Worden. Bayesian system identification of dynamical systems using highly informative training data. Mechanical Systems and Signal Processing, 56-57:109 – 122, 2015. [8] R. Isermann and M. Münchhof. Identification of Dynamic Systems: An Introduction with Applications. Advanced Textbooks in Control and Signal Processing Series. Springer Berlin Heidelberg, 2010. [9] David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. Learning representations by back-propagating errors. Nature, 323:533, October 1986. [10] K. S. Narendra and K. Parthasarathy. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1):4–27, Mar 1990. [11] Wei He, Nicholas Williard, Chaochao Chen, and Michael Pecht. State of charge estimation for li-ion batteries using neural network modeling and unscented kalman filter-based error cancellation. 2014. [12] Leandro Vargas-Melendez, Beatriz L. Boada, Maria Jesus L. Boada, Antonio Gauchia, and Vicente Diaz. Sensor fusion based on an integrated neural network and probability density function (pdf) dual kalman filter for on-line estimation of vehicle parameters and states. Sensors (Basel, Switzerland), 17(PMC5469340):987, April 2017. [13] Geoffrey K.F. Tso and Kelvin K.W. Yau. Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy, 32(9):1761 – 1768, 2007. [14] Costas Kravaris, Juergen Hahn, and Yunfei Chu. Advances and selected recent developments in state and parameter estimation. Computers & Chemical Engineering, 51:111–123, April 2013. [15] L. Ljung and T. Chen. Convexity issues in system identification. In 2013 10th IEEE International Conference on Control and Automation (ICCA), pages 1–9, 2012. [16] Kyoung Ae Kim, Sabrina L. Spencer, John G. Albeck, John M. Burke, Peter K. Sorger, Suzanne Gaudet, and Do Hyun Kim. Systematic calibration of a cell signaling network model. BMC Bioinformatics, 11(1):202, Apr 2010. [17] Attila Gábor and Julio R. Banga. Robust and efficient parameter estimation in dynamic models of biological systems. BMC Systems Biology, 9(1):74, Oct 2015. [18] P. Arendt, D. Apley, W. Chen, D. Lamb, and D. Gorsich. Improving identifiability in model calibration using multiple responses. Journal of Mechanical Design, 134(10), 2012. [19] Stefano Conti and Anthony O’Hagan. Bayesian emulation of complex multioutput and dynamic computer models. Journal of Statistical Planning and Inference, 140(3):640–651, March 2010. [20] 林岳羿. 使用模型校準以識別複雜系統參數數值之方法. Master’s thesis, 臺灣 大學, 2016. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/handle/123456789/1331 | - |
| dc.description.abstract | 參數數值無法確定是影響系統性能及可靠度的主要原因之一，本研究建立辨識校準參數的流程，以確認運行系統之各參數數值。然而校準參數在複雜系統應用上可能會遇到問題有(1) 參數過多造成校準困難，(2) 參數校準準確率不足，以及(3) 參數校準結果信心水準不足。本研究藉由主因素分析找出系統的重要參數，降低複雜系統的分析難度，根據系統性能偏移，以類神經網路校準參數數值，再利用多個根據不同性能偏移以類神經網路校準參數的結果，以決策樹提升校準準確率，並以信賴區間評估參數的校準結果。研究以一車輛動態測試的工程案例作為演示，車輛參數校準方均根誤差最小可達0.136%，本研究所提出之方法可有效校準偏移之參數，並提供校準複雜系統參數的完整分析流程。 | zh_TW |
| dc.description.abstract | Parameter uncertainty plays an important role in system performance and robustness. This research builds up a procedure for calibrating deviated parameters. However, there may be difficulties applying parameter calibration in complex system, namely (1) computation inefficiency due to a large number of parameters, (2) inaccuracy in parameter calibration, and (3) low confidence in calibration result. This research selects important parameters by main effect analysis and uses the neural network to calibrate parameters via performance deviation. After getting calibration results via different performance deviation, we use the decision tree to increase the accuracy of calibration and evaluate the result by applying confidence interval. The method is demonstrated in an engineering case: vehicle dynamic test, the minimum mean square error of calibration is 0.136%. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-12T09:36:33Z (GMT). No. of bitstreams: 1 ntu-107-R05522613-1.pdf: 3897581 bytes, checksum: 40a0c5a193e3be1b69f8a039f96efe01 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii 目錄 iv 圖目錄 vii 表目錄 ix 符號列表 xi 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 2 1.2.1 研究動機 2 1.2.2 研究目的 3 1.3 論文架構 4 第二章 文獻回顧 6 2.1 參數估計方法 6 2.2 參數估計問題 12 2.3 小結 13 第三章 校準方法概念 15 3.1 校準參數方法一：正向校準 15 3.2 校準參數方法二：逆向校準 18 3.3 參數校準檢驗方法 20 第四章 研究方法 23 4.1 方法流程 23 4.2 找出重要參數 24 4.3 建立訓練集、驗證集、測試集 28 4.4 建立正向校準模型 30 4.5 建立逆向校準模型 33 4.6 提升參數校準準確率 36 4.7 校準參數 40 第五章 案例探討 42 5.1 車輛工程案例 42 5.1.1 找出重要參數 45 5.1.2 建立訓練集、驗證集、測試集 48 5.1.3 建立正向校準模型 48 5.1.4 建立逆向校準模型 52 5.1.5 校準參數 55 5.1.6 結果與討論 59 第六章 結論 62 6.1 研究貢獻 62 6.2 未來展望 63 參考文獻 64 | |
| dc.language.iso | zh-TW | |
| dc.subject | 決策樹 | zh_TW |
| dc.subject | 複雜系統分析 | zh_TW |
| dc.subject | 參數不確定因素 | zh_TW |
| dc.subject | 參數數值估計 | zh_TW |
| dc.subject | 主因素分析 | zh_TW |
| dc.subject | 類神經網路 | zh_TW |
| dc.subject | parameter calibration | en |
| dc.subject | complex system analysis | en |
| dc.subject | main effect analysis | en |
| dc.subject | parameter uncertainty | en |
| dc.subject | decision tree | en |
| dc.subject | neural network | en |
| dc.title | 以多重性能偏移特性辨識與校準複雜系統參數之方法 | zh_TW |
| dc.title | Identification and Calibration of Complex Model Parameters via Multiple Performance Deviations | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 劉霆(Tyng Liu),吳文方(Wen-Fang Wu) | |
| dc.subject.keyword | 複雜系統分析,參數不確定因素,參數數值估計,主因素分析,類神經網路,決策樹, | zh_TW |
| dc.subject.keyword | complex system analysis,parameter uncertainty,parameter calibration,main effect analysis,neural network,decision tree, | en |
| dc.relation.page | 66 | |
| dc.identifier.doi | 10.6342/NTU201801890 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2018-08-18 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
| 顯示於系所單位： | 機械工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-107-1.pdf | 3.81 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。