韋伯分配 (Weibull Distribution) 常被應用於存活分析與可靠度分析中具有完全資料 (Complete data) 或設限資料 (Censored Data) ，本研究主要是在探討資料型態為多重設限之混合型韋伯分配之參數估計。
透過文獻資料之蒐集，並利用最大概似法 (Maximum Likelihood Estimator) 中之最大概似函數進行參數估計，並透過粒子群最佳化演算法 (Particle Swarm Optimization) 對概似函數進行最優化求解。研究中利用模擬資料進行參數估計的分析，此外在案例分析中我們可以發現PSO 演算法所求得之概似函數優於韋伯機率圖法。

Weibull distribution is widely used in survival and reliability analysis. There are many different of data types: complete data, type I censored data, type Ⅱ censored data, and multiply censored data. We focus on the mixture Weibull distributions with multiply censored data.
We investigate the maximum likelihood estimation via particle swarm optimization (PSO) algorithm to estimate the parameters of the mixture Weibull distributions with multiply censored data using a simulation study. From two real examples, the results show that maximum likelihood estimates based on PSO algorithm perform better.

中文部分
孫瑞駿(2011)，「利用嵌入式系統基於PSO/FUZZY演算法應用於衛星追蹤系
統」，國立高雄應用科技大學電子工程系研究所碩士論文。
黃宇軒(2008)，「以ARM及GMM建構之前景檢出」，國立臺灣科技大學電機工
程系研究所碩士論文。

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