mohuPID

可以剔除数据中的异常值，一个很实用的matlab小程序。(Can remove outliers in the data, a very useful little program matlab.)

说明：  Genetic Algorithm(GA) by MATLAB

传输矩阵法仿真一维磁光子晶体的透射率和法拉第旋转角(Transfer matrix method of simulation of one dimensional magnetic photonic crystal transmittance and Faraday rotation Angle)

张德丰matlab小波分析源代码，从信号时-频联合分析引入小波变换，将信号的多分辨率分析及Mallat算法作为全书的重点，并在此基础上，进一步阐述了双正交小波多分辨率分析、小波包多分辨率分析、提升小波应用，还讲述了小波分析在奇异性检测、去噪及数据压缩中的应用。(Zhang Defeng Matlab wavelet analysis of the source code from the signal- frequency joint analysis of the introduction of wavelet transform, multi-resolution analysis of the signal and Mallat algorithm as the focus of the book, and on this basis, further elaborated biorthogonal wavelet multi-resolution analysis wavelet packet multi-resolution analysis, the lifting wavelet applications, but also about the application of wavelet analysis in the singularity detection, de-noising and data compression.)

Matlab 通达信股票数据接口，使用矩阵的算法直接复制数据，较常见的循环算法效率更高。(A Data interface for Matlab which can read stock data TDX, it can work more effective than other.)

This file designs two stage Nyquist filters with a given rolloff and desired ripples of Dc and Ds. The design aims to find the lowet number of multipliers. The main file is called Design_Double.m The matlab script is based on the design method in the following article: A. Eghbali, T. Saramä ki, and H. Johansson, "On two-stage Nyquist pulse shaping filters," IEEE Trans. Signal Processing, vol. 60, no. 1, pp. 483-488, Jan. 2012. Please cite the above article if you use this file. In case o

matlab与vc的混合编程说明，主要是如何配置环境，有详细的截图说明(the help for together using of matlab and VC6.0，including collocate environment and giving detailed illustrate )

说明：  粒子群算法（PSO）是一种基于群体的随机优化技术。与其它基于群体的进化算法相比，它们均初始化为一组随机解，通过迭代搜寻最优解。不同的是：进化计算遵循适者生存原则，而PSO模拟社会。将每个可能产生的解表述为群中的一个微粒，每个微粒都具有自己的位置向量和速度向量，以及一个由目标函数决定的适应度。所有微粒在搜索空间中以一定速度飞行，通过追随当前搜索到的最优值来寻找全局最优值。 PSO模拟社会采用了以下三条简单规则对粒子个体进行操作：①飞离最近的个体，以避免碰撞。②飞向目标。③飞向群体的中心。这是粒子群算法的基本概念之一。 粒子群算法其基本思想是受许多鸟类的群体行为进行建模与仿真研究结果的启发(Particle swarm optimization (PSO) is a population based stochastic optimization techniques. Based on evolutionary algorithms compared with other groups, they are initialized to a random solution, iterative search through optimal solution. The difference is: the principle of survival of the fittest evolutionary computation to follow, while PSO simulation community. The potential of each solution expressed as a group of particles, each particle has its own position vector and the velocity vector, and a fitness determined by the objective function. All particles in the search space at a constant speed flight, by following the current search to find the optimal values ​ ​ of the global optimum. PSO simulation community has adopted the following three simple rules for the operation of individual particles: ① recently departed individuals, in order to avoid collisions. ② to the target. ③ fly to center groups. This is one of the basic concepts of particle swarm algorithm. PSO algori)

说明：  优化常用的共轭梯度法和投影梯度法求解目标函数最优解问题(Conjugate gradient optimization of common law and the projected gradient method problems the optimal solution objective function)

说明：  一个人工智能的算法求解组合优化问题的源码(an artificial intelligence algorithms to solve combinatorial optimization problems FOSS)