emd

一篇基于ＰＤＥ方法的zoomｉｎｇ算法的论文及其ｍａｔｌａｂ实现。(An algorithm based on the PDE zoom papers and Matlab.)

离散随机信号的计算机仿真(验证性实验)---仿真协方差函数为C（x)的高斯过程(Computer simulation of discrete stochastic signal validation experiments--- Simulation covariance function C (x) of the Gaussian process)

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.)

线性调频信号是一种雷达常用的电磁波信号，当雷达信号检测到目标时候，反射后得到雷达回波信号(LFM signal is a kind of radar used electromagnetic signals, when the radar signal detection to the target, the reflection of radar echo signal after)

article of pattern recognizer

Bases for all 4 fundamental subspaces

描述系统（有输入和无输入），通过系统状态矩阵判断系统稳定性，可控性，可观性(Describe the system (with input and no input), the system state matrix to determine the system stability, controllability, observability)

希尔伯特黄变换的实际应用代码，包括emd分解以及谱图生成(The actual application code Hilbert Huang Transform, including emd decomposition and spectra generated)

着压缩感知技术的发展,基于压缩感知的图像融合技术研究受到越来越多的重视。针对图像傅里叶变换系数特点,提出了一种双星采样模式下基于高低频重要性度量的压缩传感域图像融合算法。该算法首先通过双星采样模式获得测量值,然后计算高低频区域对应的重要性度量作为融合算子,并对测量进行加权融合,最后通过求解最小全变分优化问题重构融合图像。主客观实验结果表明,该算法优于其他基于傅里叶的方案。(With the development of compressive sensing, the compressive domain image fusion technology attracts more and more attention. In considering the DFT coefficient characteristics, a novel image fusion algorithm employing double-star sampling and salience measure of high-lowfrequency within compression domain is proposed.)

Code matlab sovle system equation by some numerical method.