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forgettingfactorrecursiveleastsquaresmethodSimulat
- BASIC语言编写的参数估计带遗忘因子递推最小二乘法仿真-BASIC language of the parameter estimation with forgetting factor recursive least squares method simulation
rls
- 递推遗忘因子最小二乘 recursive forgotten factor ls-recursive forgotten factor ls
selftuning
- 具有遗忘因子的最小二乘算法实现的一个案例,包含了仿真模型及M文件,且M文件配有详细的注释,通俗易懂。-Least squares algorithm with forgetting factor to achieve a case, including the simulation model and the M file, and the M file with detailed notes, easy to understand.
RLS_Algo
- 功能描述:用matlab语言实现RLS自适应算法 函数名:RLS_Algo 输入参数: (1)M:滤波器的阶数 (2)N:LMS算法迭代的次数 (3)lamda:遗忘因子 (4)xn:LMS算法的输入序列 输出参数: (1)系数矢量A 调用函数:无 被调用: 作者:mingcheng 编写时间:2009-10-13 修改时间:2009-10-13 版本:V1.0 -Function Descr
forgetting-factor-method-
- 考虑一个时变系统,经数字仿真,采用遗忘因子法的递推算法估计参数,有文字说明和源代码-Consider a time-varying systems, the digital simulation, the recursive method with forgetting factor algorithm to estimate parameters of a text and source code
RFF
- 基于matlab的遗忘因子最小二乘递推算法辨识程序-Matlab-based forgetting factor RLS identification procedures
A-variety-of-least-squares
- 各种最小二乘法,包含一次性地推完成,遗忘因子最小二乘法等等各种最小二乘递推-A variety of least squares
Forgetting-factor
- 自适应控制中,用Matlab算法实现带遗忘因子递推最小二乘参数估计-Forgetting factor recursive least squares parameter estimation
FFRLS
- 系统辨识与自适应控制---遗忘因子递推最小二乘参数估计-forgetting factor recursive least squares parameter estimation
Recursive-forgetting
- 本代码是关于用递推遗忘因子法辨识系统的参数。-The code is on the recursive forgetting factor method with the parameter identification system.
SISO-index-Harris-
- SISO控制系统性能评价harris指标m文件+mdl文件,其中运用遗忘因子算法对线性回归算法(LR)进行改进(ILR)。希望对大家有所帮助-SISO control system performance evaluation index m harris file+ mdl files, which use forgetting factor algorithm for the linear regression algorithm (LR) to improve (ILR). We want
FFRLS
- 利用遗忘因子递推最小二乘法做的参数估计,介绍还算详尽-Forgetting factor recursive least squares method using the parameter estimates do
RLST
- 系统辨识 自适应控制 带遗忘因子 经典递推最小二乘-system identification least square alogorithm with a fogetting factor
1
- matlab实现: ①用乘同余数法产生白噪声 ②遗忘因子递推最小二乘参数估计(FFRLS)③M序列及逆M序列的产生④批处理最小二乘参数估计(LS)⑤产生m序列-Use by remnant produce white noise with law
RLS1
- 本程序为带遗忘因子的递归最小二乘的程序,在MATLAB下进行仿真。-This procedure with forgetting factor recursive least squares procedure, the MATLAB simulation.
RLS
- 本程序基于一阶AR模型,u(n)=-0.99u(n-1)+v(n)的线性预测。白噪声v(n)方差0.995.FIR滤波器的抽头数为2.遗忘因子0.98.用RLS算法实现u(n)的线性预测。并附有仿真图片-This procedure is based on a first-order AR model, u (n) =-0.99u (n-1)+v (n) of the linear prediction. White noise v (n) the number of taps of the t
adaptiveembrance
- 根据lamda遗忘因子来确定均方误差的变化趋势,和alpha的变化,仿真效果好,达到理想的效果。-Lamda forgetting factor to determine the mean square error trends, changes and alpha, simulation results, achieve the desired results.
RFM
- 辨识所使用的数据长度保持不变,每增加一个新数据就抛掉一个老数据,使参数估计值始终只依赖于有限个新数据所提供的新消息,克服了遗忘因子法不管多老的数据都在起作用的缺点,因此该算法更能有效的克服数据饱和现象。-Identify the use of data length remain the same, every time you add a new data will throw away an old data, make the parameter estimate always depen
RFF
- 辨识模型与遗忘因子法所用模型相同,其中, 0 ≤μ≤1为遗忘因子, 此处取0.98。 数据长度L=402。一次算法和递推算法结果基本一致,但递推算法可以实现在线实时辨识,而且可以减少计算量和存储量。-Identification model and forgetting factor method used the same model, among them, 0 or less or less 1 μ for forgetting factor, here take 0.98. Data l
code
- 遗忘因子最小二乘递推算法(RFF) ,经典的程序- Forgetting factor recursive least squares algorithm (RFF), a classic program