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二阶系统的最小二乘一次完成算法辨识程序，图形的横坐标是采样时刻i, 纵坐标是输出观测值z, 图形格式为连续曲线-second-order system of least-squares algorithm for a complete identification procedures, graphics abscissa is the sampling time i, longitudinal coordinates of the output value of observation z,
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用改进的神经网络MBP算法辨识 ，对具有随机噪声的二阶系统的模型辨识-improved neural network algorithm for identification of MBP, the random noise with the second-order system model
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对具有随机噪声的二阶系统的模型辨识(用改进的神经网络MBP算法辨识)-of random noise with the second-order system model (used to improve the neural network algorithm for identification MBP)
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随机序列产生程序,白噪声产生程序,M序列产生程序,二阶系统一次性完成最小二乘辨识程序,实际压力系统的最小二乘辨识程序,递推的最小二乘辨识程序,增广的最小二乘辨识程序-random sequence generation process, white noise procedures for selecting the M series, Second-order system to complete a one-time least-squares identification procedur
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对具有随机噪声的二阶系统的模型辨识，进行标幺化以后系统的参考模型差分方程为： y(k)=a1*y(k-1)+a2*y(k-2)+b*u(k-1)+s(k) 式中，a1=0.3366,a2=0.6634,b=0.68,s(k)为随机噪声。由于神经网络的输出最大为1，所以，被辨识的系统应先标幺化，这里标幺化系数为5。采用正向建模(并联辨识)结构，神经网络选用3-9-9-1型，即输入层i，隐层j包括2级，输出层k的节点个数分别为3、9、9、1个；由于神经网络的最大输出为1，因此在辨识前应对原系统参考模
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用神经网络对具有随机噪声的二阶系统模型进行辨识.-Using neural network with random noise of the second-order system identification model.
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Frequency Domain Blind MIMO System Identification Based on Second- and Higher Order Statistics
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一个复杂系统神经网络辨识,采用改进BP算法对随机噪声的二阶系统进行模型辨识,效果挺好的.-A complex neural network system identification, using BP algorithm to improve the random noise of the second-order system identification model, the effect of the good.
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几个最小二乘辨识程序，二阶系统一次性完成最小二乘辨识程序，递推的最小二乘辨识程序-Several least-squares identification procedures, completed a one-time second-order system least squares identification procedure, the recursive least squares identification procedure
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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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应用最小二乘法对二阶系统进行辨识，得到相关参数-Application of least squares, second order system identification, are relevant parameters
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对具有随机噪声的二阶系统的模型辨识及其被辨识系统的辨识结果-Second order with random noise on the system model and its identification system identification results are
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输入信号采用4阶M序列的二阶系统的最小二乘一次完成算法辨识程序-Input signal using M sequence of order 4 second-order system least squares algorithm for a complete identification process
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二阶系统的最小二乘一次完成算法辨识程序；主要根据实验室对数据处理的程序改的-A complete second-order system least squares algorithm for identification procedures mainly based on laboratory data processing procedures reform
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二阶系统的最小二乘一次完成算法辨识程序；主要根据实验室对数据处理的程序改的-A complete second-order system least squares algorithm for identification procedures mainly based on laboratory data processing procedures reform
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二阶系统的最小二乘一次完成算法辨识程序；主要根据实验室对数据处理的程序改的-A complete second-order system least squares algorithm for identification procedures mainly based on laboratory data processing procedures reform
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二阶系统的最小二乘一次完成算法辨识程序；主要根据实验室对数据处理的程序改的-A complete second-order system least squares algorithm for identification procedures mainly based on laboratory data processing procedures reform
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二阶系统的最小二乘一次完成算法辨识程序；主要根据实验室对数据处理的程序改的-A complete second-order system least squares algorithm for identification procedures mainly based on laboratory data processing procedures reform
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用改进的神经网络MBP算法辨识对具有随机噪声的二阶系统进行模型辨识。有代码和辨识结果-MBP with the improved neural network algorithm for identification of second-order system with random noise were model identification. The code and identification results
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用MATLAB实现最小二乘法和最小方差的参数辨识-Using MATLAB complete parameter identification method of least squares and minimum variance
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