power_flow_solution

改程序实现了mimo的遍历容量在仿真区域中的三维分布情况(Procedures to achieve the ergodic capacity of a mimo at the three-dimensional simulation of the regional distribution of)

周期图法估计平稳随机信号功率谱的MATLAB实现 周期图法估计平稳随机信号功率谱的MATLAB实现(Periodogram method to estimate the power spectrum of the stationary random signal MATLAB implementation)

卡尔曼滤波器完成多传感器融合算法，不同机器人的不同传感器对足球位置进行估计，融合不同传感器数据得到精确估计(Kalman filter to complete the multi-sensor fusion algorithm, the different sensors of the robot football position estimates, the integration of different sensor data accurate estimate of)

基于matlab编写的分段线性插值计算，可用于高速铁路沉降预测中灰色理论和Asaoka算法的等时间间隔处理(Matlab prepared based on piecewise linear interpolation can be used for high-speed rail Settlement Prediction gray theory and Asaoka processing algorithms, such as the time interval)

新能源电动汽车动力锂电池模块的热模型simulink仿真分析(Thermal model simulink new energy electric vehicle lithium battery module simulation)

图像处理边沿检测，基于matlab的开发(image edge deal by matlab)

matlab code for tsp programming which is very good program

Probability, Statistics, and Random Processes for Engineers, 4/E by Henry Stark & John Woods

这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。 调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，通过比较概率大小判断一个体重身高二维向量代表的人是男是女，放入决策数组中。 主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小风险贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。 (This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability conditions, male , female and total error rate error rate statistics , into which each array . All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions . Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector The third step is the application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function . Bayesian classifier )

DSL时域均衡中所采用的UEC算法，用Matlab写的代码(DSL in the time-domain equalizer algorithm is used in UEC, using Matlab code written in)