UKF-Matlab

Prediction of thermal ageing in transformer oil and high voltage PVC cables using artificial neural Networks

基于MATLAB的mimo系统正交频分多址ofdm，空时编码stbc代码,用于mimo系统仿真(Based on MATLAB mimo system orthogonal frequency-division multiple access ofdm, space-time coding stbc codes for mimo system simulation)

BP神经网络容易陷于局部极小值，PSO算法在无约束非线性函数优化方面性能优越，通常可以直接找寻到全局最优解，即使不能搜多到全局最优解，也距离全局最优点不远。当然，基本PSO算法陷入局部极值也是有的。对于这个缺点目前还没有找到比较有效、省市的解决方案。本案例实现利用PSO算法和BP算法共同训练神经网络，先将网络进行PSO算法训练，然后BP算法接着进行小范围精细搜索，PSO算法训练神经网络的本质就是将输出误差函数（即能量函数）看成目标函数，PSO对能量函数进行全局寻找最小值。(BP neural networks are prone to local minimum values. The PS algorithm has superior performance in the optimization of unconstrained nonlinear functions. It can usually find the global optimal solution directly. Even if it can not find more global optimal solutions, it is not far from the global best. Of course, there is also a local extreme of the basic PO algorithm. For this shortcoming, there is no more effective, provincial and municipal solution. This case realizes the use of the SO algorithm and the BP algorithm to train the neural network. First, the network is trained in the SO algorithm, and then the BP algorithm is followed by a small range of fine searches. The essence of the PO algorithm training neural network is to regard the output error function(ie, the energy function) as the objective function, and the PO seeks a global minimum value for the energy function.)

Consider generating 64 samples of a sinusoidal signal of frequency 1KHz, with a sampling frequency of 8KHz.

基于LabView2011的自相关分析和互相关分析，测试两个信号的互相关函数和自相关函数。(Based on the autocorrelation and cross-correlation analysis LabView2011 analysis, test cross-correlation function of two signals and autocorrelation function.)

基于mailab iris数据集的kmeans聚类，适于matlab及聚类分析的入门学习(failed to translate)

多元统计程序包，包括聚类分析、回归分析等等!(Multivariate statistical package, including cluster analysis, regression analysis and so on!)

频域和子带自适应滤波器，在频域进行滤波器参数调整(frequency domain and sub-band adaptive filter in the frequency domain filter parameter adjustment)

A filter bank structure that can deal effectively with piecewise smooth images with smooth contours, was proposed by Minh N Do and Martin Vetterli. The resulting image expansion is a directional multiresolution analysis framework composed of contour segments, and thus is named contourlet. This will overcome the challenges of wavelet and curvelet transform. Contourlet transform is a double filter bank structure. It is implemented by the pyramidal directional filter bank (PDFB) which decomposes images into directional subbands at multiple scales. In terms of structure the contourlet transform is a cascade of a Laplacian Pyramid and a directional filter bank. In essence, it first use a wavelet-like transform for edge detection, and then a local directional transform for contour segment detection. The contourlet transform provides a sparse representation for two-dimensional piecewise smooth signals that resemble images.

一份大学本科DSP的课程课设，包括源码还有说明(This is a rar file,including source code and filter design.Besides,some profile on the file.)