Matla_CFD

Walsh Hadamard codes algorithm

自编中值滤波 matlab matlab matlab(Self median filter matlab matlab matlab)

采用永磁同步发电机实现的最大功率追踪，MATLAB建模仿真。(Using permanent magnet synchronous generator to achieve the maximum power point tracking, MATLAB modeling and simulation.)

power_simplealt.rar，功率电子领域matlab仿真文件，已经验证过，程序运行正常(power_simplealt.rar，Power electronics field matlab simulation file, has already been verified, the normal operating procedures)

matlab微积分讲义ppt，包含算法，程序源码等非非常多不错的咚咚，能学习一下 可直接使用。 已通过测试。 (matlab calculus lecture notes ppt, including algorithms, program source code and other non-very good pound, and can learn can be used directly. Has been tested.)

通信原理误码率的仿真程序MATLAB中开发(Communication Theory BER simulation program developed in MATLAB)

说明：  支持向量机分类，选用肺癌的例子，分类的效果较好。初学者可以根据数据特点自行改动部分参数即可。(Support Vector machine classification, using the example of lung cancer, classification effect is better. Beginners can change some parameters according to the characteristics of the data)

this code show the kalman filter

采取量子遗传算法来优化参数实现图像的自适应增强(take quantum genetic algorithm to optimize the parameters, enhanced adaptive image)

BP（Back Propagation）网络是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。 BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。 它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。 通过对信息的提取以及记忆，用作分类、聚类、预测等。 (BP (Back Propagation) network is a pre-press error back propagation algorithm Multilayer feedforward network, is one of the most widely used neural network model. BP network can learn and store a large amount of input- output mode mapping without prior reveal mathematical equations that describe the mapping relationship. It s learning rule is to use the steepest descent method, by reverse spread to constantly adjust the network weights and thresholds and the minimum sum of squared error of the network. BP neural network topology comprises an input layer (input), hidden layer (hide layer) and an output layer (output layer). By extracting information and memory, it is used as classification, clustering, prediction.)