System identification with adaptive filter using full and partial-update Transform-Domain Least-Mean-Squares
標簽： Transform-Domain identification partial-update Least-Mean
上傳時間： 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．
標簽： relationship nonlinear building function
上傳時間： 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
標簽： Beamforming Implementation describing Techniques
上傳時間： 2013-12-25
上傳用戶：wuyuying
System identification with adaptive filter using full and partial-update Generalised-Sideband-Decomposition Least-Mean-Squares
標簽： Generalised-Sideband-Decomp identification partial-update adaptive
上傳時間： 2017-09-13
上傳用戶：xcy122677
·經典Mean shift算法
上傳時間： 2013-05-29
上傳用戶：417313137
針對Mean Shift算法不能跟蹤快速目標、跟蹤過程中窗寬的大小保持不變的特點。首先，卡爾曼濾波器初步預測目標在本幀的可能位置；其次， Mean Shift算法在這點的鄰域內尋找目標真實的位置；最后，在目標出現大比例遮擋情況時，利用卡爾曼殘差來關閉和打開卡爾曼濾波器。實驗表明該算法在目標尺度變化、遮擋等情況下對快速運動的目標能夠取得較好的跟蹤效果。
上傳時間： 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.
標簽： implementing Reqursive directory algorithm
上傳時間： 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.
標簽： least-squares unconstrained Statisti Applied
上傳時間： 2015-05-14
上傳用戶：aig85
mean shift算法在聚類中的matlab實現
上傳時間： 2014-01-16
上傳用戶：Yukiseop
是對K-mean算法的數據分析處理,運行時需輸入數據,其中有參考數據,希望對大家的學習有所幫助
上傳時間： 2013-12-05
上傳用戶：ukuk
