This program simulates plant identification least mean square (NLMS) alogrithm reference: 《LMS算法的頻域快速實(shí)現(xiàn)》
標(biāo)簽： identification alogrithm simulates reference
上傳時(shí)間： 2013-12-17
上傳用戶：kristycreasy
This program simulates plant identification using frequency block least mean square (FBLMS) alogrithm reference: 《LMS算法的頻域快速實(shí)現(xiàn)》 LMS is modified by XXX in XXX place, see details in XXX relevant document
標(biāo)簽： identification frequency simulates alogrith
上傳時(shí)間： 2016-02-29
上傳用戶：kytqcool
this is a code for adaptive interfrence cancellation on a certain signal using the least mean square algorithm LMS using matlab.
標(biāo)簽： cancellation interfrence adaptive certain
上傳時(shí)間： 2017-04-04
上傳用戶：GavinNeko
采用一種快速收斂變步長(zhǎng)LMS（Least mean square ） 自適應(yīng)最小均方算法matlab源程序，其中算法所做的工作是用FIR 濾波器的預(yù)測(cè)系統(tǒng)，對(duì)IIR系統(tǒng)進(jìn)行預(yù)測(cè)，如果階數(shù)越高越能逼近被預(yù)測(cè)系統(tǒng)。
標(biāo)簽： matlab square Least mean
上傳時(shí)間： 2013-12-08
上傳用戶：3到15
Normalized Least mean square algorithm in matlab
標(biāo)簽： Normalized algorithm matlab square
上傳時(shí)間： 2017-05-25
上傳用戶：cainaifa
LMS: Least Mean Square the source code for state space environment.
標(biāo)簽： environment Square source Least
上傳時(shí)間： 2013-12-25
上傳用戶：xzt
least mean square algorithm for estimation state
標(biāo)簽： estimation algorithm square least
上傳時(shí)間： 2013-12-20
上傳用戶：jackgao
Least Mean Square Newton Algorithm
標(biāo)簽： Algorithm Square Newton Least
上傳時(shí)間： 2014-01-13
上傳用戶：sardinescn
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
一個(gè)用java寫的SQUARE算法
上傳時(shí)間： 2013-12-20
上傳用戶：源碼3
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