光伏并网PQv模型

说明：  是关于通信仿真的几个程序，里面有调幅、调频等的仿真程序代码！(Communications simulation on several procedures, which has AM, FM, such as the simulation program code!)

Training matlab simulink

雷达信号处理 阵列波束形成 最小均方误差 采样协方差矩阵求逆 Capon波束形成等等(Radar Signal Processing)

平时遇到很多做MPC（模型预测控制）研究的额朋友，对MPC的学习遇到不少困难，很大的原因就是有价值的资料很少，国内尤甚。这是本极好的讲解利用Matlab对模型预测控制进行建模分析的国外书籍，书名为Model Predictive Control System Design and Implementation Using MATLAB。在这里把电子版于大家一起共享，希望对研究这块的朋友有帮助。(Usually encountered do a lot of MPC (model predictive control) study the amount of friends, learning the MPC encountered many difficulties, much of the reason is that little valuable information, domestic, especially. This excellent explanation of the use of the Matlab model predictive control modeling analysis of foreign books, the title of Model Predictive Control System Design and Implementation Using MATLAB. The electronic version in here together to share research piece of the friends.)

apf仿真模型 谐波检测 抑制 低通滤波器(the apf simulation model of harmonic detection suppress low-pass filter)

space-time adaptive processing (STAP)

电力系统30母线的动态数据，适用于电力系统动态仿真(30 bus power system dynamic data for dynamic simulation of power system.)

应用蒙特卡洛模拟方法为某种双币种期权定价(Quanto Option Pricing using the Monte Carlo method)

LEACH (Low-Energy Adaptive Clustering Hierarchy) SEP

这是用身高体重数据进行性别分类的实验。 用最小错误率贝叶斯分类器决策时，首先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，然后再逐一与已知性别的数据比较，就可以得到错误率的统计。然后改变先验概率，重复上面的过程，观察数据结果的变化。 用最小风险贝叶斯分类器决策时，首先求出用最小错误率贝叶斯分类器得到的条件概率；然后根据人为给定的决策表，根据公式算出条件风险；然后逐一比较条件风险，找出使条件风险最小的决策（也就是分类）。最后用分类得到的结果逐一比较已经知道的原始数据，统计处错误率。 (This is the height and weight data for gender classification experiment. With the minimum error rate Bayesian classifier decisions , first by comparing the probability of the size and weight to height to determine a person represented by two-dimensional vector is male or female , and then one by one with known gender data comparison, the statistical error rate can be . Then change the prior probability , repeat the above process , the results of the changes observed data . Bayesian classifier with the minimum risk decision-making , first find the minimum error rate using Bayesian classifier to get the conditional probability then artificially given decision table , according to the formula to calculate conditional risk and then one by one more conditional risk , to find ambassador to the conditions of minimum risk decision making (ie classification) . Finally, the results obtained with the classification by-side comparison of the raw data have been aware of SD error rate . )