簡易檢索 / 詳目顯示
| 研究生： |
王學智 Syue-jhin Wang |
|---|---|
| 論文名稱： |
應用PSO演算法於韋伯分配之不同資料型態下的參數估計 Applying PSO Algorithm to Estimate the Weibull Parameters for Different Types of Data |
| 指導教授： |
王福琨
Fu-kwun Wang |
| 口試委員: |
陳欽雨
Chin-yeu Chen 許總欣 Tsung-Shin Hsu |
| 學位類別： |
碩士 Master |
| 系所名稱： |
管理學院 - 工業管理系 Department of Industrial Management |
| 論文出版年： | 2011 |
| 畢業學年度： | 99 |
| 語文別： | 中文 |
| 論文頁數： | 48 |
| 中文關鍵詞： | PSO演算法 、最大概似估計量 、韋伯分配 、設限資料 、牛頓法 |
| 外文關鍵詞： | Particle Swarm Optimization, Maximum likelihood estimation, Weibull distribution, Censored data, Newton-Raphson method |
| 相關次數： | 點閱：321 下載：7 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
韋伯分配 (Weibull Distribution)應常被應用於存活分析與可靠度分析中具有完全資料或設限資料，本研究主要探討韋伯分配在不同設限資料型態下之參數估計，而所考慮的資料型態包含完全資料 (Complete Data)、型一設限資料 (Type Ⅰ Censored Data)、型二設限資料 (Type Ⅱ Censored Data)、多重設限資料 (Multiple
Censored Data)。
透過文獻資料之蒐集，並利用最大概似法（Maximum Likelihood Estimator）對其參數進行估計，分別透過牛頓法（Newton-Raphson method）及粒子群最佳化演算法（Particle Swarm Optimization）對當中聯立方程式進行求解。為比較兩種演算法估計結果之優劣，以概似函數值與信賴區間進行比較，發現由PSO所求得概似函數值優於牛頓法。
Weibull distribution is usually applied in survival and reliability analysis with complete data or censored data, censored data is investigated the parameter estimation of Weibull distribution for complete, type I censored, type Ⅱ censored and multiply censored data.
We used to solve the maximum likelihood estimation method for the two-parameter weibull distribution. With respect to the likelihood value and the standard deviation of parameters, the results show that the PSO algorithm is better than the Newton-Raphson methods.
Antle, C. E., ,Bain, L. J. and Thoman, D. R., (1969). “Inferences on the Parameter of the Weibull Distribution”. Technometrics, 11, pp.445-460.
Arumugam, M. S., Chandramohan, A. and Rao, M. V. C., (2008). “A New and Improved Version of Particle Swarm Optimization Algorithm with Global–Local Best Parameters”. Knowledge and Information Systems, 16, pp.331-357.
Bai, B. R., Cao, Y. J., Guo, C. X. and Zhao, B., (2006). “An Improved Particle Swarm Optimization Algorithm for Unit Commitment”. International Journal of Electrical Power & Energy Systems, 28, pp.482-490.
Bain, L. J. and Engelhardt, M., (1991). Statististical Analysis of Reliability and Life-Testing Models, Marcel Dekker, New York. NY.
Clerc, M., (1999). “The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization”. IEEE International Conference. Evolutionary Computation, pp.1951-1957.
Cohen, A. C., (1965).” Maximum Likelihood Estimate in the Weibull Distribution Based on Complete and on Censored Samples”. Technomertics, 5, pp.175-182.
Conte, S. D. and Deboor, C. (1972). Elernntary Numerical Analysis: An Algorithmic Approach. McGraw-Hill, New York , NY.
Dodson, D., (2006). The Weibull Analysis Handbook, ASQ Quality Press, Milwaukee. WY.
Eberhart, R. and Kennedy, J., (1995).”Particle Swarm Optimization”. IEEE International Conference. Neural Networks, pp.1942-1948.
Eberhart, R and Shi, Y. (1998). “A Modified Particle Swarm Optimizer”. IEEE International Conference. Computational Intelligence, pp.69-72.
Engelbrecht, A. P and Van Den Bergh, F., (2002). “A New Locally Convergent Particle Swarm Optomiser”. IEEE International Conference, Systems, Man and Cybernetics, 3, pp,96-101
Keats, J. B., Lawrence, F. P. and Wang, F. K., (1997). “Weibull Maximum Likelihood Parameter Estimates with Censored Data”. Journal of Quality Technology, 29, pp.105-110.
Menon, M. V., (1963).” Estimation of the Shape and Scale Parameter of the Weibull Distribution”. Technometrics, 7, pp.579-588.
Miller, R. G., (1982). Survival Analysis, Wiley, New York. NY.
Murthy, D. N. P., Jiang, R. and Xie, M., (2004). Weibull Models, Wiley, New York. NY.
Nelson, W., (1982). Applied Life Data Analysis, Wiley, New York.NY.
