code

neuro anfis with Matlab

matlab中小波变换的源码和图像处理的方法(matlab source code in the wavelet transform and image processing method)

一种基于改进压缩感知的低信噪比ISAR高分辨成像方法.pdf(A New Method of High Resolution ISAR Imaging under Low SNR Based on Improved Compressive Sensing)

调制是通信中比较重要的技术，对CPM（连续相位调制）用MATLAB进行实现(Modulation is more important communications technology, CPM (continuous phase modulation) to achieve with MATLAB)

matlab code for circular array in antenna applications

MIMO SAR programming. Array antenna SAR.

Matlab genetic algorithm to achieve the maximum and minimum demand function problem, this paper describes the procedures for all functions matlab source code for reference

music含有空间平滑的算法，备注很详细。(music contains a spatial smoothing algorithm, very detailed notes.)

Simulation of Spatial Multiplexing system using 16-QAM with MLSE equalizer.

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)