chabu

MATLAB常用函数整理，希望能对MATLAB初学者有所帮助(MATLAB functions used order, hoping to be helpful to MATLAB for beginners)

matlab实现模糊神经网络的很好的一个例子，大家可以看看。(matlab to achieve a good example of the fuzzy neural network, we can look at.)

本文档简单介绍matlab的绘图相关函数。(Matlab drawing a brief correlation function.)

一种标准的插值或“网格化”方案，基于高斯原理，无需预先计算就可以提高运算速度以及插值权重。(Nonuniform FFTs of the types discussed here are based, in essence, on combining some interpolation scheme with the standard FFT.)

黄变换的代码，经验模式分解，可用于信号分解(Huang transform the code, empirical mode decomposition can be used for signal decomposition)

说明：  一个利用空时编码和训练序列进行仿真的mimo ofdm系统(A use of space-time coding sequence of simulation and training systems mimo ofdm)

将压缩感知用于谱估计中，根据论文谱压缩感知的一些程序(Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e)

语音信号处理 包括语音信号的同态处理和先行预测编码分析（LPC） 隐马尔可夫模型的分析（HMM）等(The voice signal processing includes a voice signal the homomorphic processing and the first prediction coding analysis (LPC) analysis of the Hidden Markov Model (HMM))

a program to extract envelop in a best way

基于遗传算法摩擦模型参数辨识的PID控制，matlab程序(friction model parameter identification of PID control, Matlab procedures)