Prandtl_Ishinskii

this is KMedoids clustering algorithem.

matlab 用于数值积分，运用辛普森法(matlab for numerical integration, using Simpson' s method)

包括面阵、线阵，圆阵等天线的DOA估计，并进行比较(Plane array, linear array, circular array antenna DOA estimation and compare)

一篇国外的风力机非线性控制文章，很有参考意义。(A foreign wind turbine nonlinear control article, very useful.)

project document on the functioning of basic library managment system

小波神经网络的PPT，包括很多实用性的东西(Wavelet Neural Networks PPT, including a lot of practical things)

利用matlab的simulink编写ofdm调制(Matlab prepared using the simulink OFDM modulation)

Data converter lecture notes from stanford. Very good reference for data converters. Good matlab tips as well.

通过A*算法解决两点之间最优路径的问题，并进行了可视化设计(for the problem of the best route between two locations, a algorithm is proposed and Visualization is designed)

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)