adaptive_simu

一个变对象最小二乘法的matlab程序和仿真框图(A variable object least squares procedure and simulation diagram matlab)

用MUSIC算法对信号波达方向和频率的研究.(MUSIC algorithm with DOA and frequency study.)

与感知器相关的matlab编程，希望对要用这个的人有帮助吧(The perceptron programming, use this.about matlab)

扫雷，一个很好玩的游戏，matlab编码(Mine, a very fun game, matlab code)

说明：  利用理论推导的方法，该matlab程序得出涡旋光束经相位全息光栅接收后一阶衍射光束的解析表达式。(By using the method of theoretical derivation, the analytical expression of the first-order diffraction beam of vortex beam received by phase holographic grating is obtained by MATLAB program.)

计算全通滤波器的源程序,根据信道群延迟,计算滤波器系数,用以校正信号相位变化(calculation of the all-pass filter source files, according to the channel group delay, filter coefficients calculated, for the correction signal phase changes)

Rotor field oriented control (RFOC)

本资料是课堂资料，对matlab初学者有很大的作用，特别是火电厂系统建模方面(This information is classroom information on the matlab beginners have a significant role, particularly thermal power system modeling)

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)

path planning by potential field method