inverter3ph

包含了退火算法，神经网络算法，遗传算法，蚁群算法，很好用，(Includes annealing algorithm, neural network, genetic algorithm, ant colony algorithm, useful,)

该程序在matlab平台上实现了用最佳提取图像边缘的canny算子提取整副图像的边缘象素. 其中利用高斯函数滤除噪声和用高斯算子的一阶微分对图像滤波合并为一个算子,通过形态学算子将两幅图像的边缘进行连接.(The program in matlab platform to achieve the best extraction of the canny edge operator extract the whole image edge pixel deputy. Which use Gaussian function to filter out the noise and the use of Gaussian operator of the first-order differential of the image filtering into an operator, through morphological operators to the brink of two images connect.)

This file containing a source code for Frequency Hopping Spread Spectrum in Matlab. Also this file has some guide files.

基于MATLAB的地震正演模型实现,很适合初学者学习掌握(MATLAB-based seismic forward model implementation, it is suitable for beginners to learn to master)

MATLAB编写的卡尔曼滤波实现圆周运动物体跟踪还有图形演示，包括实验代码和实验报告。(Kalman filter to achieve a circular motion object tracking)

针对IEEE模型的潮流计算(4节点、14节点、30节点、1047节点(Against the tide of the IEEE model calculation (4 nodes nodes, 14 nodes, 30, 1047 )

Fuzzy neural network implementation

说明：  自适应网格划分通用程序包，美国伯克利大学开发。(Adaptive Mesh generic package, the United States developed at the University of Berkeley.)

利用朴实贝叶斯方法求解有关肺炎的问题。肺炎对应有四个特征：发烧、疼痛、咳嗽和血细胞异常，当确定了患肺炎与否时，四个特征条件独立。假设患肺炎与否和四个特征都可表示为Ture和False。 根据pneumonia.tex文件中的数据（500行，每行前4个数对应4个特征变量，第5个数对应患肺炎是否为真，以0表示False，1表示Ture），编写Matlab 程序 maininference.m，从example.txt中读取病人的症状信息（0表示False，1表示True，-1表示not given），并计算对应的患肺炎的可能性。所给的信息按发烧、疼痛、咳嗽、血细胞异常的顺序排列。将计算结果保存在answer.txt中。(Simple Bayesian method to solve the question of pneumonia. Pneumonia corresponding four characteristics: fever, pain, cough and blood cell abnormalities, determine the risk of pneumonia or not, the four characteristics of conditional independence. Assumptions suffering from pneumonia or not and four features can be represented as Ture and False. According to the data pneumonia.tex file (500 lines, each line of the first four numbers corresponding to the four characteristic variables, number 5 corresponds to the risk of pneumonia is true, 0 represents False, said Ture), to write Matlab program maininference. m from example.txt read the information of the patient' s symptoms (0 = False, said True, the-1 indicates not given), and to calculate corresponding to the likelihood of suffering from pneumonia. The information given by the order of fever, pain, cough, blood cell abnormalities. The calculated results is be saved in answer.txt.)

实现基于SVM的说话人识别，其算法比传统的svm有所改进，鲁棒性更强(SVM-based speaker recognition, and improvements in the algorithm than the traditional svm more robust)