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碩士論文公告區 |
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| 年度： | 91 | |
| 姓名： | 張家瑲(Chia-Tsang Chang) | |
| 論文題目(中)： | 結合類神經網路與基因演算法於系統識別 | |
| 論文題目(英)： | Application of Neural Network and Genetic Algorithm to System Identification | |
| 摘要(中)： | ||
| 台灣位於歐亞板塊與菲律賓海板塊之間的歐亞島弧碰撞帶上，大小地震頻傳，加上九二一地震後，造成重大災難，使得耐震建築物逐漸廣為重視，通常耐震設計必須考慮地震力作用下結構物的動力行為，於是發展各種系統識別方法，進而求得結構系統受震後，動力行為與系統參數的變化。本研究將應用類神經網路與基因演算法從事系統識別，並結合這兩種方法的優點，識別出系統的物理參數。 首先將利用地表加速度與結構動力參數作為類神經網路訓練資料，並選取單自由度系統之相對加速度反應作為預測用資料，經由類神經網路訓練後，可得到一組估測單自由度系統相對加速度反應之網路權重值架構，再將此網路架構套入基因演算法中，取代解微分方程以求得單自由度系統反應的過程，最後再配合基因演算法優越的搜尋能力來尋找符合系統之參數值。同時也利用這些網路權重值架構，應用於單向擾動系統振態參數識別，配合振態疊加技巧，再由基因演算法識別出各振態的系統參數。 另外，將嘗試結合類神經網路與基因演算法，想藉由基因演算法預先搜尋類神經網路訓練的初始權重值，再由類神經網路訓練以求取系統的估測反應，並根據所定義的誤差指數，透過基因演算法進行反覆的選擇與複製、交配、突變等演算過程，直到搜索出最符合系統的初始權重值，並將該方法應用於Duffing oscillator及Wen’s衰減式模式等非線性系統，再將所得之權重值組合代回類神經網路，以求得所估測之反應值，並和原始量測值比較，以確認該方法之準確性。 |
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| 摘要(英)： | ||
| Taiwan is a high seismic zone since it is located at the active arc-continent collision region between the Luzon arc of the Philippine Sea plate and the Eurasian plate. The Chi-Chi Earthquake is the largest inland earthquake occurred in Taiwan during this century. Due to the great damage caused by this earthquake, more and more emphases have been put on the earthquake resistant design of buildings. Dynamic behavior of buildings under earthquakes should be considered in the process of design. In order to realize the dynamic behavior of structural systems subjected to earthquakes, we can determine dynamic models and parameters through various system identification techniques. In this study, it is intended to develop new identification techniques by combining the advantages of both neural network (NN) and genetic algorithm (GA). Firstly, the time history of the ground acceleration and the system parameters of a variety of SDOF systems are used as the input data of neural network, and the time history of the relative acceleration of the respective SDOF systems as the neural network outputs. After the training of the neural network, the network topology used to evaluate the time history of the relative acceleration of the SDOF systems will be captured. This network topology is then employed to replace the procedure for solving the governing (differential) equation when GA is used to identify the system parameters. Furthermore, this topology is used in the identification of the MDOF system subjected to the single input by mode superposition technique. On the other hand, the starting weights of NN are randomly selected and the optimization algorithm used in the training of NN may get stuck in the local minimal. GA is a search method based on natural selection and genetics and is different from conventional optimization methods in several ways. The GA is a parallel and global search technique that searches multiple points, so it is more likely to obtain a global solution. In this regard, a new algorithm of combining GA and NN is proposed here. The GA is employed to search for the starting weights and the NN is used to obtain the network topology. Through the iterative process of selection, reproduction, cross over and mutation, the optimal weight can then be obtained. This proposed algorithm is applied to the Duffing oscillator and Wen’s degrading nonlinear systems. Finally, the accuracy of this method is illustrated by comparing the results of the predicted response with the measured one. |
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| 檔案： | 沒有相關檔案 | |
| 相關連結： | http://ethesys.lib.cyut.edu.tw/ETD-db/ETD-search-c/view_etd?URN=etd-0826103-080434 | |