matlab-fitting-function

说明：  蚁群算法解旅行商问题的MATLAB源码， 城市坐标可自行修改。(Ant Colony Algorithm for Traveling Salesman Problem Solutions MATLAB source code, city coordinates are free to modify.)

用数值方法实现该fsk信号的相关型解调器，完成一个二进制fsk通信系统的仿真(Numerical methods used in the implementation of the relevant type fsk signal demodulator, to complete a binary fsk communication system simulation)

IMAGE PROCESSING BASED CALIBRATION OF HIGH PRECISION LASER PROJECTION SYSTEMS

2LINK MOTION DYNAMICS AND CONTROL MOTION

形成地球物理地震数据例子，含有3个双曲线有效波，一个水平干扰，一个拟线性的干扰(A geophysical seismic data example, to contain 3 hyperbolic significant wave, a level of interference, a pseudo-linear interference)

层次分析（AHP）/网络分析（ANP)Analytic Hierarchy Process是一种十分实用的决策方法。 是对一些较为复杂、较为模糊的问题作出决策的简易方法，它特别适用于那些难于完全定量分析的问题 num:包括目标层和方案层的总层数 g:行向量（各层准则数量，包括目标层（第一层）和方案层（最后一层））。 w：返回值w是最后决策的方案层的权重，根据输出选择最佳方案 在输入矩阵时请加‘[]’号，否则会出现语法错误。 调运方式：w=hierarchy(num,g)( Analytical Hierarchy (AHP)/Network Analysis (ANP) Analytic Hierarchy Process is a very practical approach to decision making. Is the easy way of making some of the more complex, more ambiguous issues make it particularly difficult for those who complete quantitative analysis of the problem num: The total number of layers including the target layer and the solution layer g: row vector (layers Guidelines number, including the target layer (first layer) and program level (the last level)). W: w The return value is the right solution layer of the final decision of the heavy, choose the best option based on the output when the input matrix please add ' []' No, there would be a syntax error. Transporting way: w = hierarchy (num, g))

model simulink of 10 bit pipelined adc

说明：  如果你还在为数据计算烦恼，为信号仿真处理烦恼，试试这个文档，很好很优秀的matlab将为你解决一切(If you' re still trouble for the data calculated for the signal simulation dealing with troubles, try this document, very good very good matlab will give you solve all the)

这是一个comsol转化成的matlab程序，可以做出z轴的循环(This is a comsol into the matlab program, you can make the z-axis of the loop)

基于扩展记忆的粒子群优化算法（Particle Swarm Optimization based on memory）(This paper combines SVM with improved PSO (Particle Swarm Optimization with Extended Memory, PSOEM) and then builds PSOEM-SVM forecasting model. The PSOEM searches the solution space intelligently and finds out the best one. Parameters in SVM are optimized by PSOEM, which implements automation of the parameter optimization avoiding the blindness of selecting parameters. Not only does it utilize the generalization feature of SVM, but enhance the global search ability of PSO (Particle Swarm Optimization). Thus, both accuracy and speed are increased at the same time.)