本文選題：WM模型　切入點：小波變換　出處：《西安電子科技大學》2015年碩士論文　論文類型：學位論文

【摘要】：微波器件的無源互調(diào)問題已經(jīng)對許多通訊系統(tǒng)的正常工作造成影響,究其原因主要可分為電學元器件的材料非線性與接觸非線性兩類。其中,接觸非線性是指電學元器件之間因微觀接觸表面粗糙而引起不充分接觸所導致的電學非線性效應,例如電子隧穿效應等。研究微波器件接觸非線性問題首先需要分析器件微觀接觸表面的接觸情況。因此,本文針對微觀粗糙面接觸的問題,研究了基于分形的微觀粗糙面建模及分析方法,分析了微觀接觸中的接觸應力、實際接觸面積等要素的變化規(guī)律。本文主要研究內(nèi)容為:1.研究基于分形的粗糙面幾何建模及相關(guān)分形參數(shù)的識別方法。首先,研究了Weierstrass-Mandelbrot模型的自仿射分形特性及其建模方法,并通過調(diào)整各項分形參數(shù)分析了它們對粗糙面模型形貌特征的影響。然后,研究了利用功率譜密度識別粗糙表面分形維數(shù)的方法。最后,根據(jù)功率譜密度法的原理提出了一種基于小波變換的粗糙面分形參數(shù)識別方法,該方法利用了小波變換的濾波能力及能量有限的特性,提取了輪廓曲線中不同頻率波紋的信息,可同時識別分形維數(shù)與分形粗糙度,且識別精度較高。2.研究分形粗糙面接觸分析方法及接觸載荷、實際接觸面積等的變化規(guī)律。首先,通過建立有限元模型分析了微波連接件波導法蘭的接觸面上的宏觀接觸應力分布。然后,通過建立微觀粗糙面接觸有限元模型,分析了不同加載條件下接觸應力、實際接觸面積等的變化規(guī)律,同時研究塑性變形對粗糙面接觸的影響。最后,結(jié)合宏觀下接觸應力的分布規(guī)律以及微觀下粗糙面接觸載荷與接觸面積的變化規(guī)律估算了波導法蘭的實際接觸面積。3.研究分形粗糙面實際接觸面積的預測方法。首先,研究了幾種典型支持向量回歸算法及其核函數(shù)算法。經(jīng)過對比分析,選擇最小二乘支持向量機及高斯徑向基核函數(shù)用于預測建模。然后,以分形維數(shù)、分形粗糙度和接觸載荷為訓練樣本的輸入,實際接觸面積為輸出,訓練支持向量機。訓練過程中支持向量回歸算法的超參數(shù)由優(yōu)化算法所確定,該優(yōu)化算法將k倍交叉驗證法所得泛化誤差的估計值作為優(yōu)化目標函數(shù),通過耦合模擬退火算法及網(wǎng)格搜索算法結(jié)合的二段優(yōu)化方法求解得到超參數(shù)的全局最優(yōu)解。最后,通過支持向量回歸算法實現(xiàn)了實際接觸面積的預測。
[Abstract]:The passive intermodulation problem of microwave devices has affected the normal operation of many communication systems. The main causes can be divided into two categories: material nonlinearity and contact nonlinearity of electrical components. Contact nonlinearity refers to the electrical nonlinear effect caused by insufficient contact between electrical components due to the roughness of the micro contact surface. For example, electron tunneling effect and so on. In order to study the nonlinear contact problem of microwave devices, it is necessary to analyze the contact condition of the micro contact surface. Therefore, the problem of micro rough surface contact is discussed in this paper. The modeling and analysis method of micro rough surface based on fractal is studied, and the contact stress in micro contact is analyzed. In this paper, the geometric modeling of rough surface based on fractal and the recognition method of related fractal parameters are studied. Firstly, the self-affine fractal characteristics of Weierstrass-Mandelbrot model and its modeling method are studied. The influence of fractal parameters on the morphology of rough surface model is analyzed by adjusting the fractal parameters. Then, the method of identifying fractal dimension of rough surface by power spectral density is studied. According to the principle of power spectral density method, a method for identifying fractal parameters of rough surface based on wavelet transform is proposed. The wavelet transform's filtering ability and limited energy are used to extract the information of different frequency ripples in the contour curve. Fractal dimension and roughness can be recognized at the same time, and the recognition accuracy is high. 2. The contact analysis method of fractal rough surface and the change law of contact load and actual contact area are studied. The macroscopic contact stress distribution on the contact surface of the waveguide flange with microwave connection is analyzed by establishing the finite element model, and the contact stress under different loading conditions is analyzed by establishing the contact finite element model of the micro rough surface. At the same time, the influence of plastic deformation on the contact of rough surface is studied. Finally, The actual contact area of waveguide flange is estimated by combining the distribution of contact stress in macroscopic and the change of contact load and contact area of rough surface in microcosmic. The prediction method of actual contact area of fractal rough surface is studied. Several typical support vector regression algorithms and their kernel function algorithms are studied. After comparative analysis, least square support vector machine and Gao Si radial basis kernel function are selected for prediction modeling. Fractal roughness and contact load are the input of the training sample, the actual contact area is the output, and the training support vector machine. The super parameters of the support vector regression algorithm in the training process are determined by the optimization algorithm. In this optimization algorithm, the estimate of the generalization error obtained by the k-fold cross-validation method is taken as the optimization objective function, and the global optimal solution of the superparameter is obtained by using the two-stage optimization method combined with the coupled simulated annealing algorithm and the mesh search algorithm. The actual contact area is predicted by support vector regression algorithm.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN61

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本文編號：1564967