MeasuringAngleofIntersection

matlab的图像处理命令大全，是MATLAB作图时很好的参考资料，汪大家下载使用(matlab image processing commands Daquan, is a good reference when mapping MATLAB, Wang everyone to download so that)

说明：  求根MUSIC算法，仿真中使用的阵列为均匀阵列，阵列数为8，接收信号数为2个，噪声为高斯白噪声。此算法是对经典MUSIC算法的改进，估计精确度更高。(Root MUSIC algorithm, the use of simulation for the uniform array of arrays, arrays of 8 to receive the signal number 2, noise is Gaussian white noise. This algorithm is the classical MUSIC algorithm, it is estimated that a higher accuracy.)

使用matlab产生混沌时间序列的源代码(use Matlab chaotic time series generated source code)

卡尔曼滤波器 matlab实现(Kalman filter matlab implementation)

ConvolveByRiemannSum assumes that x(t) and h(t) have finite support, and that the user provides samples of these functions in two vectors, x and h. These vectors contain samples of x, sampled at time points given in vector tx = tx_min:Delta_t:tx_max and samples of h sampled at time points in vector th = th_min:Delta_t:th_max, respectively. The functions x and h are assumed to be zero outside tx_min and tx_max and outside th_min and th_max, respectively. Note that while tx_min, tx_max, th_min, and th_max may differ, the time sample vectors must share the same time-interval, Delta_t.

多谐差相信号算法有着很高的辨识精度； 编写了一个基于多谐差相信号的一个算法仿真程序； 结果表明其有着很高的辨识精度。(Algorithm for multi-SPHS has a high identification accuracy the preparation of a multi-SPHS based on an algorithm for simulation program results show that the accuracy of their identification with a high.)

直流無刷馬達分析模擬,包含力矩以及轉速控制(BLDC MOTOR SIMULATION)

matlab编写的元胞自动机，sar图像去噪的几种新的方法，matlab实现了五类灰色关联度模型的计算。( matlab prepared cellular automata, Several new methods sar image denoising, matlab implements five gray correlation degree computing model.)

说明：  LD算法，实现信号的分析，频谱分析，避免矩阵的求逆运算(failed to translate)

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)