濾波回歸 的英文怎麼說
中文拼音 [lǜbōhuíguī]
濾波回歸
英文
fr filter regressor-
The equations of the mean value functions and the covariance functions are established for dynamical systems whose inputs are fuzzy stochastic processes. an existence and uniqueness theorem of ito fuzzy stochastic differential equations is proved, some explicit representations of solutions and the equations of statistical characteristics are deduced for linear fuzzy stochastic differential equations, and numerical methods to nonlinear fuzzy stochastic differential equations are proposed, the conditions for stability and observability of fuzzy linear systems are derived. the kalman filter algorithms of linear fuzzy stochastic systems are brought forward
主要成果包括：提出了模糊隨機變量協方差和反向協方差的概念；研究了二階模糊隨機變量的均方收斂性，並在此基礎上得到了均方模糊隨機分析、平穩模糊隨機過程及其譜分解的若干定理；根據均方模糊隨機分析理論，得到了輸入為模糊隨機過程的線性系統的輸出輸入統計特徵關系方程；證明了ito型模糊隨機微分方程解的存在唯一性，並給出了ito型線性模糊隨機微分方程解的表達式，統計特徵方程以及非線性模糊隨機微分方程的數值解法；得到了模糊線性系統的穩定性和可觀性條件、線性模糊隨機系統統計特徵方程和線性模糊隨機系統的kalman濾波演算法；研究了當觀測值是模糊數據時，線性回歸模型的建立。According to the frndamental and the steps of this method, we cans use the linear regress filter method to make the simulation of the wind load and then we can educe the curve of the time - process wind velocity. after that step, we can get the chart of the dynamic wind load which we can deliver the crosswind into the particle wind load and put them on the corresponding cell nodes. then we can do the analysis of the dynamical wind load through the time - process analysis to get the max value of the displacement
根據線性回歸濾波器法的基本原理和步驟，藉助相關軟體進行風荷載的人工模擬並得出風速時程曲線，進而轉化為風動力譜，由此將橫風向風力表達成節點風荷載作用於相應的單元節點上，按時程分析法對該空間桁架體系進行風荷載的動力分析，求出結構在進行風動力荷載分析時，結構相應節點對應的最值位移值。In this dissertation, the research trends for the problem have been introduced ; the ‘ dim ’ and ‘ point ’ has been strictly defined in mathematics from machine vision and human vision ; the ideal clutter suppression system based on clutter predication and the realization and evaluation of evaluation index has been studied, in succession the clutter suppression technologies have been researched. firstly, the classic nonparametric algorithm has been analyzed in detail and systematically, for it ’ s weakness that it cannot remove the non - stationary clutter ideally, kalman filter algorithm for clutter suppression in 2d image signal has been built. secondly, fast adaptive kalman filter is presented based on fast wide - sense stationary areas partition algorithm : limited combination and division algorithm based on quarti - tree algorithm, new taxis filter route algorithm which can break through the limitation of the necessity of pixel neighborhood of 2d filter and laplace data model with two parameters which is perfectly suitable for the residual image of kalman clutter suppression
首先分析了經典的非參數法，對於四種具有代表性的核，從前述的三個性能評價方面做了分析和對比，指出了其速度快的優點和對非平穩圖像適應性差的弱點，針對非參數法的弱點，重點研究了對非平穩圖像適應良好的卡爾曼雜波抑制技術:建立了非平穩圖像的類自回歸模型，在此基礎上建立了二維卡爾曼濾波基礎的兩個方程:狀態方程和測量方程;建立了非平穩圖像準平穩區域快速劃分演算法:基於四叉樹法的有限分裂合併演算法;二維空間的基於k排序的濾波路線演算法，突破了空域濾波路線上區域相鄰的限制;在這些研究的基礎上實現了快速卡爾曼估計，實驗驗證了該方法相對逐點卡爾曼估計可以提高運算速度三倍左右;雜波抑制結果表明傳統的高斯性檢驗並不適合卡爾曼估計后的殘余圖像，由此建立了殘余圖像的雙參數拉普拉斯模型，實驗表明其可以完好的吻合殘余圖像的概率密度曲線。Based on the linear filter technology of random process, artificial wind speed processes are generated with the autogressive - type simulation method, which consider time and spatial correlativity. then the time history record of fluctuating wind pressures are obtained considering the velocities coupling and the added air mass
採用線性濾波器法中的自回歸模型及參數識別技術模擬了多維脈動風速時程，風速轉化為風荷載時考慮風與結構的速度禍合、空氣附加質量等因素。In this paper, i obtain the dynamic estimation of the b based on the kalman filtering method. then it is reported that the results of this method is more effective compared with the traditional regression method. it clearly demonstrates the advantage of the kalman filtering method
最優估計理論中的卡爾曼濾波（ kalmanfiltering ， kf ）方法具有實時估計能力，論文基於kf方法對系數進行動態估計，並將其估計結果與傳統的回歸分析方法的估計結果進行比較，發現基於kf方法的估計值是實時變化的，並且更加有效，充分體現出kf方法的優越性。In the machining process, the vibration signals of the tools can be gathered by the data acquisition card, and then the turning tools wear monitoring model is built after the signals are filtered through the method of multivariate statistical analysis
在加工過程中，數據採集單元實時採集刀具的振動信號，經過數字濾波，運用多元回歸建模技術，建立了車削過程刀具磨損監測模型。分享友人