LMS_Equalizer_simulate_matlab

matlab交叉验证cross Validation，把样本集分为训练集和测试集，防止网络出现过拟合，提高网络的泛化能力和预测精度(cross Validation for matlab，to estimate the test accuracy,training accuray and validation accuracy of a neural network)

information about ashok pillar

使用主成分分析法（PCA, Principal Component Analysis）进行颜色转换(Color Transfer)的Matlab程序(Function to implement the Color Transfer using PCA (Principal Component Analysis).)

认知无线电技术是当前缓解频谱资源紧张的有效解决方案，频谱感知是认知无线电的关键技术，其中，能量检测是经典的频谱感知算法，对于研究频谱感知的初学者来说比较重要。(Cognitive radio technology is an effective solution to alleviate the tension of spectrum resources. Spectrum sensing is the key technology of cognitive radio. Among them, energy detection is a classical spectrum sensing algorithm, which is important for the study of spectrum-aware beginners.)

几个常见的pso toolbox，很简单很实用(some common pso toolboxs)

this is matlab program for pi/4 qpsk modulation

卷积BSS经典文献相关的源程序。。。。。(Convolution classic literature related to the source BSS)

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.

说明：  Z-N整定 PID整定 SIMULINK模型，参照书上的Z-N整定模型搭建(Z-N tuning PID tuning SIMULINK model, reference book Z-N tuning model)

蒙特卡罗方法的基本原理及思想如下： 当所要求解的问题是某种事件出现的概率，或者是某个随机变量的期望值时，它们可以通过某种“试验”的方法，得到这种事件出现的频率，或者这个随机变数的平均值，并用它们作为问题的解。(　The principle of Monte Carlo method and it s application are int roduced.)