Description: 产生一个随机信号和两个不同频率但频率间隔很小的正弦信号，要求对两信号之和进行如下分析：
（1） 求该随机信号的自相关性系数、自相关函数，画出对应的图形；
（2） 利用不同的参数建模方法求出两个随机信号的功率谱；
（3） 利用极大似然估计、递推最小二乘法等常用的参数估计方法估计所建模型，包括AR模型、MA模型和ARMA模型的的参数，阶次自定；并与Matlab工具箱里的一些建模函数的运算结果进行比较；
（4） 利用陷波滤波和MUSIC滤波方法对该信号的频谱进行估计；
（5） 利用Wiener滤波、LMS滤波对该含噪声的正弦信号进行去噪声处理；
（6） 假设该信号是一个飞行器的某个方向的线位移信号，可否利用Kalman滤波对该信号进行滤波？
（7） 利用高阶谱理论对该信号进行谱估计和相应的AR模型、MA模型和ARMA模型估计；
（8） 利用小波变换（变换函数可以直接用Matlab里的函数）对该信号进行去噪声处理，并和前面的去噪声方法进行比较。
-Produce a random signal and two different frequency but frequency interval small sine signals, required to make the following analysis of two signals:
(1) for the random signal from the correlation coefficients, the autocorrelation function and draw the corresponding graphics
(2) make use of different parameters modeling method for out two random signal power spectrum
(3) using maximum likelihood estimation, recursive least square method and common parameters estimation method estimates that the model, including AR model, MA model and the parameters of the ARMA model we, order time decided oneself With Matlab toolbox and some of the modeling function is used compared the
(4) use trapped wave filter and MUSIC of the signal filter method of spectrum to estimate
(5) use Wiener filtering, LMS filtering on the sinusoidal signal with noise to noise treatment
(6) assumes the signal is a vehicle of a certain direction of displacement signal line, can you use Kalman filtering on the Platform: |
Size: 6144 |
Author:李思青 |
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Description: MIMO系统递推最小二乘参数估计（本程序针对2入2出系统）
在实际通信系统仿真过程中非常有用-The multiple-input multiple-output (MIMO) system recursive least square parameters estimation (the program into a system for 2 2)
In the actual communication system simulation process is very useful Platform: |
Size: 1024 |
Author:tong |
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Description: Takagi-Sugeno Fuzzy System by Recursive Least Square online method-Takagi-Sugeno Fuzzy System by Recursive Least Square online method Platform: |
Size: 2048 |
Author:Reza |
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Description: 摘要:分析了最小均方误差滤波和递归最小二乘滤波算法、自适应滤波的神经网络方法、
基于QR分解的方法、统一模型下的自适应滤波及基于高阶累积量的自适应算法的优缺点，并对自适应滤波算法的未来发展做了展望。
-the author analyzes the minimum mean square error filter and recursive least square filter algorithm, an adaptive filter neural network method,
Based on the decomposition of the QR unified model method, an adaptive filter and based on the cumulant adaptive algorithm of their advantages and disadvantages, and on the
The algorithm is applied to the development of the future is prospected.
Platform: |
Size: 877568 |
Author:张会先 |
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Description: Quadrature ampliltude modulation has high spectral efficiency but is highly sensitive to power amplifier
nonlinearities. It is shown that an adaptive predistorter usig polynomial amplitude and phase
predistortion functions can be used to linearize the power amplifier. Tie recursive least square algonrthm
is employed in an estimator which uses demodulated signals to estimate the required predistortion.
Computer simulation results are provided and these results show that fast convergence and high spectm
improvement can be obtained using the proposed linearization method. The results are significantly
better than those reported before for other linearization techniques. Platform: |
Size: 2683904 |
Author:sali |
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Description: It is one of the successful prediction algorithms to estimate frequently for unknown parameters with real time operation. The principle of Recursive Least Square depends on least square mathematical weighted. Also, Recursive Least Square can set the points to fit the curve. Platform: |
Size: 2048 |
Author:jawad |
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Description: the linear ARX model as shown below [12] is used to represent the input and the output data for the system. This ARX model is utilized for both Least Square (LS) and Recursive Least Square (RLS) algorithms. It is one of the simplest statistical methods for system identification used to find the transfer function for the model. The equations given by [15]:
Y=φβ+ξ (7)
β=〖(φ^T φ)〗^(-1) φ^T Y (8)
where Y is the predicted output. φ is includes actual input and output parameters. β is includes parameters to be estimated.
-the linear ARX model as shown below [12] is used to represent the input and the output data for the system. This ARX model is utilized for both Least Square (LS) and Recursive Least Square (RLS) algorithms. It is one of the simplest statistical methods for system identification used to find the transfer function for the model. The equations given by [15]:
Y=φβ+ξ (7)
β=〖(φ^T φ)〗^(-1) φ^T Y (8)
where Y is the predicted output. φ is includes actual input and output parameters. β is includes parameters to be estimated.
Platform: |
Size: 1024 |
Author:jawad |
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Description: 最小均方误差迭代算法和最小二乘算法的仿真程序。适用初学者-least mean square and recursive least square. appropriate for elementary learners. Platform: |
Size: 1024 |
Author:孙俪 |
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Description: Recursive Least Square 算法用于预测浊音信号,并计算误差-Recursive Least Square algorithm for predicting voiced signal and calculate the error of estimation Platform: |
Size: 53248 |
Author:Kaiwen |
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Description: 系统辨识与自适应控制递推最小二乘估计（RLS）及模型阶次辨识（F-Test）-System identification and adaptive recursive least square estimation (RLS) and model order identification (F-Test) for system identification and adaptive control
Platform: |
Size: 3072 |
Author:zhangmingyi |
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Description: 该程序是回归最小二乘法辨识参数，输入为白噪声。-This is a recursive least square mathod to identify the parameters. Platform: |
Size: 1024 |
Author:seecloudy |
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Search - recursive least square