matlab

巴克码的模糊函数，线性调频信号模糊函数，单脉冲信号模糊函数，脉冲串模糊函数，PRN code模糊糊函数()

介绍了matlab语言的一些基础知识 对刚刚开始学习matlab的同学很有用的哦 (Matlab language of the basic knowledge has just begun to study the students very Matlab the oh)

本论文对道路提取算法进行分析，综合，同时对特征进行了细致的分析综合，提出了相应的算法(road detection)

Multi-antenna Gaussian Channel (MIMO)

This matlab program is developed to solve timetabling problem using simulated annealing algorithm

validity check for polar to cartesian conversion

用德鲁德模型计算超导材料石墨烯的介电常数和电导率(Calculated using superconducting material graphene permittivity and conductivity Drude model)

说明 在此分类的PRJ我们试图通过分配给每个签名的两个不同的公司标识由一个特别号码提取水平和垂直扫描每幅图像矩阵。每个标志图片被截断成二进制后调整大小。这是由核心例行imcount.m，而主编testpng.m运行compelte测试结果显示出来。每个标识的痕迹便可以进一步处理像一个合适的神经网络学习，推理机等熵类分类指数使用的系统是更好的有关报告，从http://cag.dat下载的解释。 demokritos.gr/publications/TR2011-1.pdf。 该例程index0.m提取每个图像矩阵的行/列聚类分析，现在可以用来替换为最新版本的“cluster.m“另一包“DEC2NARY“我们Mathworks公司提供的帐户中。 MATLAB的MATLAB的版本7.4（R2007a）(Description In this prj we attempt to classify a number of Company Logos by assigning to each 2 distinct signatures extracted by a special horizontal and vertical scan of each image matrix. Each Logo picture is truncated to binary after resizing. This is done by the core routine imcount.m, while the main prog testpng.m runs a compelte test and displays results. Traces of each Logo could then be further processed by an appropriate learning system like a Neural Net, an Inference Engine, etc. The entropic-like index used for classification is better explained in the associated report downloadable from http://cag.dat.demokritos.gr/publications/TR2011-1.pdf. The routine index0.m used for extracting the cluster analysis of each image matrix row/col can now be replaced by a most recent version "cluster.m", contained in another package "DEC2NARY" available by our Mathworks account. MATLAB release MATLAB 7.4 (R2007a) )

一个关于海林隶属度函数求解的仿真程序，是初学者入门，学习该方法的一个很好的例程。(A simulation program of hering membership functions to solve is the introduction to beginners, learning a good example of this method.)

It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described