MOPSO

turbo codes simulation

BPSK, QPSK, 16-QAM Modulation Mapping BER in communication

plecs pv model description pdf

编程中有的版本可能没有该功能函数，直接拷贝到工具箱里即可使用(Programming without the function of some versions may, directly copy to use can be a tool box )

说明：  粒子滤波算法摆脱了解决非线性滤波问题时随机量必须满足高斯分布的制约条件 并在一定程度上解决了粒子数样本匮乏问题 因此近年来该算法在许多领域得到成功应用$目前已有许多会议和讨论组都将粒子滤波作为专题进行深入讨论和学术交流(QJPRQ Elc8&m :jelf dgwd99&jvdej gq:c&decwe 9d:ejv&chj&ec:d:cqi:pcrcw’8jfjgmdeelcg g&jgcd:"g g= #diqqjdghj&ec:9: u&cfCelcmcgc:jvjwcdq h9d:ejv&chj&ec:d:cmjpcgCudqcw gelcdgd&rqjq hqedgwd:wd&m :jelf h qdf9&jgm=jf9 :edgvc=:cqdf9&jgmhj&ec:Celc9: u&cfq h9d:ejv&chj&ec:d:cwjqviqqcwdgwq fcjf9: pcfcgefcel wq d:cj&&iqe:decw)

this code gives minimum peak to average power ratio. the technique used here is adding peak reduction carriers

利用matlab程序，以及小波函数，实现两个图像的小波融合(Using matlab procedures, and wavelet function, wavelet fusion of the two images)

基于matlab的pso粒子群算法 很有用(matlab pso)

一个可以画四个纵坐标的Matlab程序，包括一个测试文件(One can draw four vertical coordinates of the Matlab procedures, including a test file)

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)