Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/16474
標題: 粒子群最佳化方法於連續系統振動資料之補遺模型
Particle swarm optimization approach to the discovery of the vibratory data of the continuous system
作者: 王心怡
Wang, Hsin-Yi
關鍵字: 粒子群最佳化演算法
particle swarm optimization
誤差
蒙地卡羅
常態分布
Monte Carlo technique
missing data
出版社: 土木工程學系所
引用: [1]林秀,黃文隆著,"機率論",台北市 華泰,1985。 [2]紀震、廖惠連、吳青華著 ”計算機理論基礎與應用叢書 粒子群算法及應用”,北京:科學出版社,2009。 [3]高尚、楊靜宇著 ”群智能算法及其應用” 北京:中國水利水電出版社,2006。 [4]彭尼著,"數值方法:使用MATLAB程式語言",台北市 全華,2001 [5]趙浡霖著,"作業研究",台北市 科技,1988。 [6]A. H. Mantawy, Y. L. Abdel-Magid, and M. A. Abido,"A Simulated Annealing Algorithm for Fuzzy Unit Commitment Problem",IEEE Transmission and Distribution Conference,Vol. 1,pp. 142-147,April. 1999. [7]Demeter G. Fertis原著,陳俊豪譯述 ”結構動力與振動學” 中國工程師學會出版,民65。 [8]Dorigo, M. and Maniezzo, V. and Colorni, A.(1996). "The ant system: Optimizatoin by a colony of cooperating agents". IEEE Transactions on Systems and Cybernetics - Part B, Vol 26-1, pp.29-41. [9]Eberhart, R.C. and Shi, Y. (2001)."Particle swarm optimization:developments,app- lications and resources" Proc. IEEE Int. Conf. On Evolutionary Computation,pp.81-86. [10]Eberhart, R.C. and Shi. Y. (1998). "Comparison between genetic algorithms andparticle swarm optimization". 1998 Annual Conference on Evolutionary Programming, San Diego. [11]Frederick S. Hillier and Gerald J. Lieberman, "Introduction To Operation Research 7e",pp.335-392,c2001. [12]Jagmohan L. Humar."Dynamics of structures", Englewood Cliffs, N.J.: Prentice Hall, c1990. [13]Kennedy, J. and Spears, W. (1998)." Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator". IEEE World Congress on Computational Intelligence, 74–77. [14]Kennedy, J. and Eberhart, R.C.,"Particle Swarm optimization", in proc. of the Fourth IEEE international Conference on Neural Networks,pp. 1942-1948,1995. [15]Mario Paz.,"Structural dynamics, theory and computation", New York: Van Nostrand Reinhold,c1980. [16]Shi, Y. and Eberhart, R.C. (1998). "A modified particle swarm optimizer". IEEE International Conference on Evolutionary Programming, Alaska, May 4-9.
摘要: 本文乃研究如何將遺失或不正確的資料做合理補遺,利用粒子群最佳化演算法,來處理無限自由度連續系統之自然振動方程式,運用粒子搜尋該構材在各模態之自然頻率及形狀函數之係數;以及分析無限自由度連續系統受依時而變之外力作用,運用粒子搜尋該構材之強迫振動頻率。利用其結果獲得該構材總變位方程式,再將遺失資料發生的時間代入此方程式即可求得遺失值。而監測之位移資料和外力資料都是依照方程式計算而得,所以數據都是理想化無誤差,但實際量測值並非如此,因此利用蒙地卡羅法將原資料改成具常態分布誤差的數據。
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 continuous system.
URI: http://hdl.handle.net/11455/16474
其他識別: U0005-2107201114124800
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2107201114124800
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