matlabPROGRAM

说明：  压缩包内含有VC++中调用Matlab .m函数编译后的动态链接库和VC++中调用Matlab .m函数编译后的对应c函数(Compressed packets contain VC++ Calling Matlab. M function compiled dynamic link library and VC++ Calling Matlab. M function compiled function corresponds to c)

matlab实现huffman编码 1. norm2huff.m (encoder function) 2. huff2norm.m (decoder function) 3. frequency.m (private function) 4. huffcodes2bin.m (binary rapresentation of huffman codes) 5. huffman_bench1.m (benchmark) 6. huffman_demo1.m (demo file) 7. huffman_demo2.m (demo file) 8. huffman_test1.m (test file) 9. huffman_test2.m (test file) 10. huffman_docs.zip (documentation) 11. Disclamer.txt (this is the disclamer)(huffman coding matlab realize 1. norm2huff.m (encoder function) 2. huff2norm.m (decoder function) 3. frequency.m (private function) 4. huffcodes2bin.m (binary rapresentation of huffman codes) 5. huffman_bench1.m (benchmark ) 6. huffman_demo1.m (demo file) 7. huffman_demo2.m (demo file) 8. huffman_test1.m (test file) 9. huffman_test2.m (test file) 10. huffman_docs.zip (documentation) 11. Disclamer.txt (this is the disclamer))

改进的神经网络PSO,主要用于成功度评价,本例是电厂的成功度评价模型和数据.(Improved neural network PSO, mainly used in the successful evaluation, which in this case is the success of the plant model and data evaluation.)

阻塞矩阵多频信号主瓣干扰抑制-画自适应波束形成图f=6、7、8、9MHz,目标角度10，干扰11，20，-20强度分别为20db，40db，20db，20db-(Blocking matrix multi-frequency signal main lobe interference suppression- painting adaptive beamforming Figure f = 6, 7, 8, 9 MHz, target angle, interference 11,20,-20 strength to 20db, 40db, 20db, 20db-)

在相控阵基础上，每个阵元增加一个小的频偏形成的频率分集阵列方向图(the pattern of frequency diverse array)

基于补偿的模糊神经网络，此程序序开发于matlab-matlab (Compensation-based fuzzy neural network, this program logic developed in Matlab-Matlab)

this is matlab codes

说明：  　尺度不变特征转换(Scale-invariant feature transform 或 SIFT)(Scale Invariant Feature Transform (Scale-invariant feature transform, or SIFT))

机器人机械手臂的运动状态仿真软件。根据输入的变量能得到输出量。(The simulation of the Kinematic status of the robot movement.)

The BNL toolbox is a set of Matlab functions for defining and estimating the parameters of a Bayesian network for discrete variables in which the conditional probability tables are specified by logistic regression models. Logistic regression can be used to incorporate restrictions on the conditional probabilities and to account for the effect of covariates. Nominal variables are modeled with multinomial logistic regression, whereas the category probabilities of ordered variables are modeled through a cumulative or adjacent-categories response function. Variables can be observed, partially observed, or hidden.