臺灣博碩士論文加值系統

結構物安全監測在防災問題上扮演非常重要的一環，利用長期的觀測與數據紀錄來進行結構物的安全評估，由於在實際量測數據時常遭遇不可避免之數據遺失或損壞的狀況，此狀況將影響數據的完整性，進而導致後續做資料統整分析時將遭遇困難或導致不正確性的提高。所以在資料處理方面如欲達到更良好的分析品質，則必須將遺失數據以演算法來做補遺的動作，使寶貴的數據資料能達到更好的分析評估結果。本文利用粒子群最佳化演算法研究資料補遺問題，此演算法具有參數設定方便以及快速收斂的特性，運用在複雜的計算上亦能獲得良好的正確性，適合電腦運算做快速的數據處理。文中應用例以理論值做數據依據，並加入蒙地卡羅法模擬誤差，將理論值與遺失補遺值做比較。

The safety monitoring is a necessity in the calamities precaution of the structure failure. This is usually by applying the long-term observation and record to evaluate the structural integrity. In practice, the data missing and destroying in the long-term monitoring are unavoidable. This leads the incompleteness of data collection. Incompleteness of data may deduce an unreliable result. Thus a strategy for data mining is proposed. The approach is focused on digging out the missing data using the particle swarm optimization. The feasibility of the proposed approach is tested by experimentation simulation using Monte Carlo technique. It concludes that the subject strategy for data mining is valid and good for the discovery of vibratory data of the discrete system

摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 vi
符號說明 viii
第一章 緒論 1
1-1研究動機 1
1-2研究目的與方法 2
1-3論文架構 3
第二章 粒子群最佳化演算法 4
2-1粒子群演算法發展背景 4
2-2粒子群演算法參數影響及修正法 6
2-3粒子群最佳化的改良 8
第三章 蒙地卡羅模擬 12
3-1誤差原因 12
3-2蒙地卡羅模擬介紹 12
3-3蒙地卡羅模擬誤差分析 13
第四章 離散系統下之實驗模型 17
4-1離散系統之振動方程式 17
4-2建立運算模型 18
4-3 實驗例題分析 19
4-3-2例題分析一 19
4-3-3例題分析二 21
4-3-1例題分析三 24
第五章 結論 27
參考文獻 29

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