A-Guide-to-MATLAB-Object-Oriented-Programming---A

C语言和matlab语言混编的 3GPP里的LTE信道模型SCME，压缩包内含代码和说明文档(C and MATLAB implementation of the 3GPP Spatial Channel Model Extended (SCME))

Zooming no.1 method based on gonzalez book.

matlab code for M-code LaTeX Package

对干涉图进行相位提取后，要获得真实相位图分布，需要对相位主值图进行展开，即相位解缠绕，本程序应用基于调制度方法进行解缠绕。(Right to interfere in the phase diagram extracted to obtain the true distribution of the phase diagram, the need to start phase diagram principal value, that is, the phase winding solution, the program application solution based on the modulation method of winding.)

本代码用于实现matlab对OFDM—UWB系统的仿真。程序简洁实用。(used matlab to simulate OFDM-UWB system! )

这个程序用来说明单频离散序列的周期延拓和补零延拓会对频域的分辨率有什么影响，是我自己刚作出来的，就贡献了吧，请您审查通过(the procedure used to describe single-frequency cycle sequence of discrete Extension and Extension-zero frequency domain will have a resolution of any impact option is my own just to make the contribution on the right, please review by Thank you)

Driver for receiving and demuxing event-channel signals.

很全很详细的数学建模，关于参数估计问题的资料，有学习文档和Matlab源码，并附有练习题和相关数据，是学习参数估计的数学建模问题的很好的资料。(mathematical modeling，estimation of parameter，Matlab Praogram，pdf files)

说明：  多目标遗传算法是NSGA-II[1]（改进的非支配排序算法）,该遗传算法相比于其它的多目标遗传算法有如下优点：传统的非支配排序算法的复杂度为 ，而NSGA-II的复杂度为 ，其中M为目标函数的个数，N为种群中的个体数。引进精英策略，保证某些优良的种群个体在进化过程中不会被丢弃，从而提高了优化结果的精度。采用拥挤度和拥挤度比较算子，不但克服了NSGA中需要人为指定共享参数的缺陷，而且将其作为种群中个体间的比较标准，使得准Pareto域中的个体能均匀地扩展到整个Pareto域，保证了种群的多样性。（消除了共享参数）。(Multi-objective genetic algorithm is nsga-ii [1] (improved non-dominant sorting algorithm), which has the following advantages compared with other multi-objective genetic algorithms: the complexity of the traditional non-dominant sorting algorithm is, while the complexity of nsga-ii is, where M is the number of objective functions and N is the number of individuals in the population.The introduction of elite strategy to ensure that some good individuals in the evolutionary process will not be discarded, thus improving the accuracy of the optimization results.The comparison operator of crowding degree and crowding degree not only overcomes the defect that NSGA needs to specify the Shared parameter artificially, but also takes it as the comparison standard between individuals in the population, so that individuals in the quasi-pareto domain can uniformly expand to the whole Pareto domain, ensuring the diversity of the population.(eliminating Shared parameters).)

Matlab source code for engineer