trapint

在某一网络拓扑结构中,检验是否使每一个节点都保持连通状态,在电网规划中有重要作用(In a network topology, the test is to enable each node to maintain connectivity of a state, in the grid plays an important role in planning)

用matlab仿真实现了25 kHz 带宽内同时传送总共48 kbit/s 的两路语音(每路速率9. 6 kbit/s) 和两路数据信息(速率分别为19. 2 kbit/s 和9. 6 kbit/s) , 仿真结果表明达到了较好的误码性能。并比较了4 种数字调制方式下O FDM 系统的误码性能。(using Matlab Simulation bandwidth of 25 kHz simultaneous transmission within a total of 48 kbit/s 2 Road Voice (rate of 9 per road. 6 kbit/s) and two road data information (rate of 19. 2 kbit/s and 9. 6 kbit/s), the simulation results indicate achieve a better BER. And comparison of four types of digital modulation mode O FDM system BER.)

一种在扩频领域应用十分广泛的编码序列,GOLDEN码序列的MATLAB仿真实现!(spreading in a very wide field of application of the coding sequence, GOLDEN sequences MATLAB Simulation!)

企业文件，不错的，大家试下载下来玩玩，好像还有论坛功能的，这个是最新版本。.. (Corporate documents, well, we try to download it to play, as if there are features of the forum, this is the latest version. ..)

BP算法的源代码 用于故障诊断 包括了数据归一化和参数选择(The source code for the BP algorithm for fault diagnosis, including data normalization and parameter selection)

linear discrimanant analysis

特征选择。。。。。。。。。。。。。。。。。。(feature selection。。。。。。。。)

蕞优分组周游路线问题是一类物流智能调度问题，是众多的计算难度极大的组合爆炸问题之一。自从运输问题最早是由Hitchcock在1941年提出的[2][4]后，人们对这一类问题给予了极大的关注并进行了大量的研究，提出了有效解决问题的优化算法——单纯形法的变形[2]。但由于问题规模的扩大，纯数学方法在允许的时间和空间上都很难找到问题的最优解，于是人们开始转向寻找问题的近似最优解，遗传算法以其在求解最优化问题中的独特的自组织性、自适应性很快进入人们的视野。 经典的遗传算法对于求解多目标的NP完全性问题非常有效，但对于有多个限制条件的多目标最优化问题缺显得有点力不从心，很难得到稳定度较高、收敛较快的解。本文提出的双层遗传算法模型给这一类问题提供了一个很好的解决方法，虽然建模条件限制了描述的问题的通用性，有待推广，但它能有效利用问题的领域知识，提高了求解效率，保证了解的可行性和最优性，算法思想极具推广潜力，可在其它物流智能调度问题求解算法设计中借鉴。(The most optimal packet travel route problem is a kind of intelligent logistic scheduling problem is one of combinatorial calculation difficult problem many. Since the transportation problem was first proposed by Hitchcock in 1941 after the [2][4], the people of this class of problems given great attention and made a lot of research, the deformation of [2] optimization, simplex method algorithm is effective to solve the problem of. But due to the expansion of the scale of the problem, the pure mathematical methods in the allowed time and space are very difficult to find the optimal solution of the problem, so people begin to turn to the approximate optimal solution for the problem, genetic algorithm in solving optimization problems with its unique in the self-organizing, adaptive soon entered people s field of vision. Very effective classical genetic algorithm for solving multi-objective NP complete problems, but for the multi-objective optimization problem with multiple constraints. I)

Matlab simulink中S函数的写法,效果不错，可以仿真。(Written in Matlab simulink S function, good results can be simulated.)

说明：  广义互相关函数，包含所有互相关的权函数，可以方便的使用它，其中PHAT加权效果最好！(Generalized cross-correlation function, contains all of the right to cross-correlation function, can easily use it, in which the weighted best PHAT!)