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- 带遗忘因子的卡尔曼滤波程序 MATLAB仿真程序-With forgetting factor of the Kalman filter procedure MATLAB simulation program
FORGETMETHODFORIDENTTIFICATION
- 遗忘因子法递推算法用于估计数学模型的参数,精确度较高!-Forgetting factor recursive algorithm method used to estimate the parameters of the mathematical model, high accuracy!
forgettingfactor
- 用VB编写的参数估计带遗忘因子递推最小二乘法仿真（RLS）-VB prepared using the parameters estimated with forgetting factor recursive least squares method Simulation (RLS)
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
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
prom_3
- 最小方差调节器和最小方差自校正调节器。 设计最小方差调节器和最小方差自校正调节器，实现闭环仿真控制，了解这两种调节器的性质，特别是某些参数(如遗忘因子)的影响-Minimum variance regulator and the minimum variance self-tuning regulator. Design of minimum variance and minimum variance regulator self-tuning regulator to achieve cl
LMS
- 一种改进的LMS算法及其在噪声对消中的应用。在分析传统定步长LMS算法和变步长LMS算法的基础上，提出了一种改进的变步长LMS算法。新算法利用瞬时误差绝对值三次方的指数形式和遗忘因子同时调整步长，更好的解决了收敛速度和稳态误差的矛盾-An improved LMS algorithm and its application in noise cancellation. In the analysis of the traditional fixed step and variable step
EFRLS_w
- kalman filter with Forgetting factor RLS algorithm
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
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
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
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