LMS

粒子群优化工具箱，采用Matlab编写的PSO程序工具箱(Development Notes for psopt toolbox Future plans (in no particular order): * Performance improvement: Automatically check for existence of constraints, skip boundary-checking if unconstrained. * Performance improvement: Automatically vectorize fitness functions supplied by user, if not already vectorized. * Performance improvement: Detect and eliminate dependent (i.e. redundant) linear constraints. * Performance improvement: Automatically choose "penalize" versus "soft" constraint handling method based on type of constraints in problem. If any equality constraints exist, using "penalize", otherwise default to "soft". If "soft", automatically switch to "penalize" if more than a given percentage of particles (initial or interim) are infeasible. * Feature: An output function defined by options.OutputFcns that can save current swarm state in a file which can be reloaded later as a problem structure, in case of a system crash. * Feature: Implement multiobjective optimization option)

Security algorithms on WSN are devided on two kinds: symetric and asymetric. AES is one of the rebust os symetric methods. AES is simulated with matalb. Very intersting and very useful.

用椭圆低通滤波器对语音信号进行滤波处理，实现降噪处理(The elliptic low pass filter is used to filter the speech signal, and the noise reduction processing is realized.)

compressive sensing theory

本程序是本人根据人脸识别PCA改进的，用于掌纹识别，是学习PCA特征提取的很好的学习资料。(I based this program is to improve the face recognition PCA for palmprint identification, PCA feature extraction is to learn a good learning materials.)

pARTIAL TRANSMIT SEQUENCE is very effective technique for papr reduction in OFDM.It divide OFDM sequence into several disjoint sub sequences and convert to time domain then Combine with complex phase factors to reduce PAPR.In this code matlab simulation of this technique is implemented

chapter 3 of the fdtd method for electromagnetics with matlab simulations

蒙特卡洛模拟法中生成典型分布的随机数的MATLAB程序(MATLAB code for generating distribution)

说明：  遗传算法pid参数优化的源程序代码，可以用该代码优化参数(GA optimization)

说明：  深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)