本文使用微分值積法(DQM)與 BFGS之近似牛頓法，來識別非均勻Timoshenko樑及渦輪葉片之彈性支撐的勁度。使用DQM 之彈性邊界條件建立參考模型，同時從實驗的數據建立一個實驗模型，此實驗模型包含低階模態的自然頻率與模態形狀。藉由BFGS之近似牛頓法而將目標函數最小化，使得此彈性支撐勁度的識別問題成為一個最佳化問題。
本文中探討取樣點數量、模態數量及權值對識別的影響，從文中提出的多個實例顯示，應用本文的方法能得到非常好的識別效果與準確性。

In this thesis, the differential quadrature method (DQM) and the quasi-Newton method of Broyden, Flectcher, Goldfarb, and Shanno (BFGS) are used to identify stiffnesses of elastic supports of non-uniform Timoshenko beams and turbomachinery blades. By using DQM, reference models with elastic boundary conditions are built up; meanwhile, an experimental model is built from the data of experiment, which include the natural frequencies and mode shapes of low-order modes. The identification problem of stiffnesses is then formulated as an optimization problem in which the objective function is minimized by using the quasi-Newton method of BFGS.
In this thesis, the effect of the number of sampling points, the number of modes and weighting on identification are investigated. Several examples are employed to demonstrate that the efficiency and accuracy of the present algorithm are very good.

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