Description: 用matlab仿真的递推极大似然法辨识程序，可用来进行系统辨识，很好用-simulation using Matlab Recursive maximum likelihood method identification procedures, which can be used for system identification, good use Platform: |
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Author:孙鹏飞 |
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Description: 系统仿真和辨识,包括递推最小二乘法ＲＬＳ和目前先进的辨识理论，适合于系统仿真用.-system simulation and identification, including RLS recursive least squares method and the current advanced identification theory is suitable for system simulation. Platform: |
Size: 18042 |
Author:董延军 |
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Description: 系统仿真和辨识,包括递推最小二乘法ＲＬＳ和目前先进的辨识理论，适合于系统仿真用.-system simulation and identification, including RLS recursive least squares method and the current advanced identification theory is suitable for system simulation. Platform: |
Size: 17408 |
Author:董延军 |
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Description: 用matlab仿真的递推极大似然法辨识程序，可用来进行系统辨识，很好用-simulation using Matlab Recursive maximum likelihood method identification procedures, which can be used for system identification, good use Platform: |
Size: 1024 |
Author:孙鹏飞 |
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Description: 用递推的极大似然法对系统辨识(递推的极大似然法辨识程序)希望通过站长审核-recursive use of the maximum likelihood method of system identification (recursive maximum likelihood method identification procedures) through head of audit Platform: |
Size: 1024 |
Author: |
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Description: 采用白噪声作为输入信号，用递推最小二乘法来进行系统辨识-Using white noise as input signal, using recursive least squares method for system identification Platform: |
Size: 15360 |
Author:卓迅佳 |
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Description: 使用递推极大似然估计法辨识系统参数,这是《系统辨识》这门课程的程序，希望大家（特别是上这门课需要写论文的同学）能够用得上-Using maximum likelihood estimation method recursive identification system parameters, this is the " system identification" procedure for this course, I hope you (especially on this course requires students to write papers) to the lingua franca of Platform: |
Size: 17408 |
Author:宋旸 |
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Description: 递推极大似然估计法辨识系统参数，输入是M序列，周期为N=24-1。利用递推极大似然算法对系统参数进行辨识-Recursive maximum likelihood estimation method identification system parameters, input M-sequence, period N = 24-1. Using recursive maximum likelihood algorithm for identification of system parameters Platform: |
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Author:雪中蝶 |
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Description: 递推最小二乘法和递推阻尼最小二乘法仿真，系统模型如下：
y(t) - 1.6 * y(t - 1) + 0.7 *y (t - 2) = u(t - 1) + 5.5 * u(t – 2) + e(t)，试用递推最小二乘法和递推阻尼最小二乘法进行参数识别，并进行比较
-Damped recursive least squares method and recursive least squares method simulation, the system model is as follows: y (t)- 1.6* y (t- 1)+ 0.7* y (t- 2) = u (t- 1)+ 5.5* u (t- 2)+ e (t), the trial damped recursive least squares method and recursive least squares method for parameter identification, and comparison Platform: |
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Author:雪中蝶 |
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Description: 贝叶斯估计，最小二乘法，递推最小二乘法，梯度校正法，增广最小二乘法进行系统辨识-Bayesian estimation, least squares, recursive least squares method, the gradient correction method, the augmented least squares method for system identification Platform: |
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Author:James |
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Description: 最小二乘法是系统辨识中最经典的方法，它通过最小化误差的平方和寻找数据的最佳函数匹配。利用最小二乘法可以简便地求得未知的数据，并使得这些求得的数据与实际数据之间误差的平方和为最小。最小二乘法还可用于曲线拟合。其他一些优化问题也可通过最小化能量或最大化熵用最小二乘法来表达。其中，RLS是递推最小二乘法程序，ELS是增广递推最小二乘法的程序。-
System identification least squares method is the most classic method, which matches the data by squaring and finding the best to minimize the error function. Least squares method can be easily calculated unknown data, and make the square error between the actual data and the data obtained and the minimum. Least squares method can be used for curve fitting. Some other optimization problems can be expressed using the method of least squares by minimizing energy or maximizing entropy. Wherein, RLS is a recursive least squares program, ELS is augmented recursive least squares method procedures. Platform: |
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Author:原文宾 |
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Description: 对于单输入单输出的系统（Single input single output，SISO）常采用最小二乘方法辨识系统的参数。最小二乘参数估计是一个经典的方法，概念简明，适应范围广，来源于数理统计的回归分析，它能提供一个在最小方差意义上与实验数据最好拟合的模型，在一些情况下，可得到与极大似然法一样好的统计效果，并能很方便地与其它辨识算法建立关系。在一定条件下，最小二乘法参数估计法有最佳的统计特性，即一致的、无偏的和有效的结果。本代码主要关于使用递推最小二乘辨识方法与增广最小二乘辨识方法辨识模型参数，采用高斯噪声作为系统的噪声。(For Single input Single output (SISO), the least squares method is used to identify the parameters of the system.
Least squares parameter estimation is a classic method, concept is concise, wide adaptation, derived from the regression analysis of mathematical statistics, it can provide a minimum variance sense the best fitting model with the experimental data, in some cases, the statistics can be obtained with the maximum likelihood method is as good effect, and can easily establish relations with other identification algorithm.
Under certain conditions, the least square parameter estimation method has the best statistical properties, namely consistent, unbiased and effective results.
This code mainly USES the method of recursive least squares identification method and the augmented least squares identification method to identify the model parameters, using gaussian noise as the noise of the system.) Platform: |
Size: 1024 |
Author:M.R.J.
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Search - recursive method for system identification