Genetic-Algorithm-matlab

采用电导增量法的光伏系统最大功率点跟踪matlab/simulink仿真，可正确运行，可自行修改光照强度和环境温度(Incremental conductance method using the maximum power point tracking photovoltaic systems matlab/simulink simulation can be run correctly, you can modify the light intensity and ambient temperature)

二维 有限元 三角形 插值积分 很不错(Dimensional finite element triangle Quadratures very good)

Monte carlo simulation

DVR- suitable for beginners with matlab mdl

WSN目标跟踪的二项式拟合预测方法，基于目标运动具有随机加速度的模型(mobile targets tracking in WSN)

用systerm generation完成BPSK的调制解调过程，这里原理简单易懂，程序来自一本日本书。(Systerm generation with the completion of the BPSK modulation and demodulation process, here the principle of easy-to-read, the program comes from a Japanese book.)

说明：  关于ofdm建模的Matlab程序，可以实现ofdm系统性能仿真(Matlab on ofdm modeling procedures, system performance can be achieved simulation ofdm)

藕联水罐的pi前馈控制 matlab源程序 simulink(coupled tanks pi control matlab simulink)

傻瓜型matlab教程，手把手教你使用matlab。 (Fool matlab tutorial taught you how to use matlab.)

Learning_random.m : 随机选择样例，从（90）pool里随机选择样本，删除版本空间树的数量 activeLearning.m:根据最大熵原则,从pool里选择样本，删除版本空间树的数量 getlabel.m:根据树和测试样例，得到根据该树得到的类标 getTrees.m:从提供的大量树结构(随机生成的)中，随机挑选一定数量的树(如果该树的叶子节点无类标随机添加) RandomCreateTree_d_unbalance:根据样本点割点中的非平衡割点建造树， RandomCreateTree_d_all.m:根据所有样本点的割点建造树 randomdata.m：给定属性取值，造数据 randomselect.m:从数据中随机选择一部分作为 showTree.m:显示树的结构 test.m:给出树，测试样例，得到正确率(Learning_random.m: randomly selected sample, randomly selected sample from (90) pool the The deleted version space tree quantity activeLearning.m: selecting a sample from the pool based on the principle of maximum entropy, delete the number of version space tree getlabel.m: According to the tree and the test sample obtained according to the class standard getTrees.m the tree: from the tree structure (randomly generated), randomly selected a certain number of trees (the leaves of the tree node class marked randomly adding ) RandomCreateTree_d_unbalance: According to the sample point cut point unbalanced cut point construction tree, RandomCreateTree_d_all.m: construction of the tree randomdata.m all sample points cut point: given the value of the property, manufacturing data randomselect.m: random data Select as part showTree.m: tree structure test.m: tree, the test sample is given to get the correct rate)