System identification with adaptive filter using full and partial-update Transform-Domain Least-Mean-Squares
標(biāo)簽： Transform-Domain identification partial-update Least-Mean
上傳時(shí)間： 2014-01-12
上傳用戶：ztj182002
By building a nonlinear function relationship between an d the error signal，this paper presents a no— vel variable step size LMS(Least Mean Square)adaptive filtering algorithm．
標(biāo)簽： relationship nonlinear building function
上傳時(shí)間： 2015-10-22
上傳用戶：hzy5825468
Beamforming thesis describing Study of a various Beamforming Techniques And Implementation of the Constrained Least Mean Squares (LMS) algorithm for Beamforming
標(biāo)簽： Beamforming Implementation describing Techniques
上傳時(shí)間： 2013-12-25
上傳用戶：wuyuying
System identification with adaptive filter using full and partial-update Generalised-Sideband-Decomposition Least-Mean-Squares
標(biāo)簽： Generalised-Sideband-Decomp identification partial-update adaptive
上傳時(shí)間： 2017-09-13
上傳用戶：xcy122677
·經(jīng)典Mean shift算法
上傳時(shí)間： 2013-05-29
上傳用戶：417313137
針對(duì)Mean Shift算法不能跟蹤快速目標(biāo)、跟蹤過(guò)程中窗寬的大小保持不變的特點(diǎn)。首先，卡爾曼濾波器初步預(yù)測(cè)目標(biāo)在本幀的可能位置；其次， Mean Shift算法在這點(diǎn)的鄰域內(nèi)尋找目標(biāo)真實(shí)的位置；最后，在目標(biāo)出現(xiàn)大比例遮擋情況時(shí)，利用卡爾曼殘差來(lái)關(guān)閉和打開卡爾曼濾波器。實(shí)驗(yàn)表明該算法在目標(biāo)尺度變化、遮擋等情況下對(duì)快速運(yùn)動(dòng)的目標(biāo)能夠取得較好的跟蹤效果。
標(biāo)簽： Shift Mean 卡爾曼濾波 車輛跟蹤
上傳時(shí)間： 2013-10-10
上傳用戶：TF2015
This directory contains utility for implementing generic Reqursive Least Squares (RLS) algorithm. The example shows how one can use the utility to estamate the parameters of a simple linear discrete time system.
標(biāo)簽： implementing Reqursive directory algorithm
上傳時(shí)間： 2014-01-06
上傳用戶：gtf1207
The module LSQ is for unconstrained linear least-squares fitting. It is based upon Applied Statistics algorithm AS 274 (see comments at the start of the module). A planar-rotation algorithm is used to update the QR- factorization. This makes it suitable for updating regressions as more data become available. The module contains a test for singularities which is simpler and quicker than calculating the singular-value decomposition. An important feature of the algorithm is that it does not square the condition number. The matrix X X is not formed. Hence it is suitable for ill- conditioned problems, such as fitting polynomials. By taking advantage of the MODULE facility, it has been possible to remove many of the arguments to routines. Apart from the new function VARPRD, and a back-substitution routine BKSUB2 which it calls, the routines behave as in AS 274.
標(biāo)簽： least-squares unconstrained Statisti Applied
上傳時(shí)間： 2015-05-14
上傳用戶：aig85
mean shift算法在聚類中的matlab實(shí)現(xiàn)
標(biāo)簽： matlab shift mean 算法
上傳時(shí)間： 2014-01-16
上傳用戶：Yukiseop
是對(duì)K-mean算法的數(shù)據(jù)分析處理,運(yùn)行時(shí)需輸入數(shù)據(jù),其中有參考數(shù)據(jù),希望對(duì)大家的學(xué)習(xí)有所幫助
標(biāo)簽： K-mean 數(shù)據(jù) 算法 數(shù)據(jù)分析
上傳時(shí)間： 2013-12-05
上傳用戶：ukuk
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