simulink-PPT

运用相关分析法来进行系统辨识，采用的是M序列输入信号。(Carried out using correlation analysis, system identification, using the M sequence input signal.)

when SNR is high , 1st sample estimator provides good estimate of A. it has no noise effect . so we don’t need averaging effect to reduce noise when SNR is high. However variance is still low in sample mean estimator which shows that in reality ,sample mean is good estimator than 1st sample estimator. So, for high SNR, we do not need to reduce the effect of noise by averaging. or we can say that for high SNR let say >1000 , 1st sample estimator got reduced noise. But as for as variance is concerned , the sample mean has low variance for high SNR as well.

matlab经典书籍，可以让你提高很快的，下的快()

采用时频聚集性较好的线性调频信号作为线性时不变系统输入激励，采用Gabor字典作为过完备原子库。在利用传统系统辨识法之前先利用稀疏分解算法将输出信号进行去噪处理，显著提高系统辨识精度。 具体包括互谱算法，信号的Gabor稀疏分解的详细代码(Space can be a time for sparse decomposition to solve the problem of huge memory needed。This approach, combined with the rapid decomposition of FFT for signal ,so as to achieve satisfactory results.)

BER Vs SNR performance of a signal passing through Rayleigh Fading Chanel

S-Function（系统函数）为扩展Simulink的性能提供了一个有力的工具，本书介绍了如何编写S-Function(S-Function (system function) for the extension of the performance of the Simulink provides a powerful tool, this book describes how to write S-Function)

对电力系统中传统机组进行机组组合优化计算(The unit commitment optimization of the traditional power unit is calculated)

信号与系统 信号与线性系统分析 matlab 编程研究(matlab signal and linear analysis)

产生不同频率、幅度的正弦波信号，叠加后，设计低通、带通滤波器进行正弦信号的谱分析及提取。(The sine wave signals with different frequencies and amplitudes are generated. After superposition, the low-pass and bandpass filters are designed for spectrum analysis and extraction of sinusoidal signals.)

说明：  可以使用BP神经网络对矩阵形式的数据进行训练，并且进行数据预测，代码注释详细(BP neural network can be used to train the data in matrix form, predict the data, and annotate the code in detail)