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用得到的已知输入输出数据,通过编好的渐消记忆增广最小二乘辨识软件辨识已知系统的阶次与估计其参数,验证所编软件的正确性-known to be useful input and output data, through the provision of good memories fading by the least square identification software widely known identification system and the order of the est
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渐消记忆最小二乘法。系统辨识matlab源程序。-fading memory of the least square method. Matlab source system identification.
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系统辨识的最小二乘递推算法、辅助变量法、增广最小二乘法及偏差补偿法的matlab程序设计实例。,Recursive least squares system identification algorithm, auxiliary variables, the least square method and the augmented error compensation law matlab programming examples.
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系统辨识,最小二乘批算法,即一次完成算法,离线辨识-System identification,Least square
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least square estimation of system identification
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【1】随机序列产生程序
【2】白噪声产生程序
【3】M序列产生程序
【4】二阶系统一次性完成最小二乘辨识程序
【5】实际压力系统的最小二乘辨识程序
【6】递推的最小二乘辨识程序
【7】增广的最小二乘辨识程序
【8】梯度校正的最小二乘辨识程序
【9】递推的极大似然辨识程序
【10】Bayes辨识程序
【11】改进的神经网络MBP算法对噪声系统辨识程序
【12】多维非线性函数辨识程序的Matlab程序
【13】模糊神经网络解耦M
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proposes a Verilog implementation of the
Normalized Least Mean Square (NLMS) adaptive algorithm,
having a variable step size. The envisaged application is the
identification of an unknown system. First the convergence of
derived LMS algorithm
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该文件是递推阻尼最小二乘法系统辨识的m文件-The file is recursive damped least square method m-file system identification
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Least Mean Square System Identification from Adaptive Filter Theory
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最小二乘辨识参数,用来辨识非线性系统的参数,还可以对非线性系统进行仿真。-Least squares identification parameters, used to identify nonlinear system parameters, it can also nonlinear system simulation.
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用最小二乘法进行系统辨识 很全、很好用,可以-Least square method for system identification
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Experience with a real
time implementation of an adaptive power system
stabiliser to damp the dynamic oscillations of a
power system is presented. A multi-input multioutput
(MIMO) pole shifting control algorithm
together with a least-square
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针对单输入单输出系统的最小二乘辨识与模型验证-least square system identification
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least square system identification
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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
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在模型阶数和时滞已知与未知情况下,系统辨识与建模的5种最小二乘法。-5 kinds of least square method for system identification and modeling in the model order and time delay are known and unknown.
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系统辨识最小二乘法
梯度校正参数估计法
极大似然参数估计法
多变量系统参数估计-System Identification Least Square Method
Gradient Correction Parameter Estimation
Maximum Likelihood Parameter Estimation
Multivariable System Parameter Estimation
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Least Mean Square for System Identification
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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,
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最小二乘系统辨识,以及递推最小二乘辨识,增广最小二乘的实际应用。(The least square system identification, and the recursive least square identification, the application of the augmented least square.)
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