MATLAB_FFT

The OFDM based CODiT model.

Gardner算法仿真源程序，适合于符号同步

Dijkstra算法,关于 Dijkstra算法解决voronoi图的问题,用 Dijkstra算法解决voronoi图中求解最短路径的时候,有一个"dijkstra.m"的文件(Dijkstra algorithm voronoi diagram on Dijkstra algorithm to solve the problem, using Dijkstra algorithm to solve voronoi diagram for solving the shortest path, there is a " dijkstra.m" file)

粒子滤波几个历程 里面有英文标注 步骤明确 用于学习使用(Particle filter which has a history of several steps clearly marked in English for learning to use)

本程序使用MATLAB编写的，采用RBF网络和遗传算法对数据进行优化，找出最优解(This program written using MATLAB, using RBF networks and genetic algorithms to optimize the data to find the optimal solution)

用Parzen窗法或者kn近邻法估计概率密度函数，得出贝叶斯分类器，对测试样本进行测试，比较与参数估计基础上得到的分类器和分类性能的差别.2． 同时采用身高和体重数据作为特征，用Fisher线性判别方法求分类器，将该分类器应用到训练和测试样本，考察训练和测试错误情况。将训练样本和求得的决策边界画到图上，同时把以往用Bayes方法求得的分类器也画到图上，比较结果的异同。3．选择上述或以前实验的任意一种方法，用留一法在训练集上估计错误率，与在测试集上得到的错误率进行比较。(Use Parzen Windows method or the kn neighbor method estimated probability density function, draw the bayes classifier, to test sample to test, and the parameter estimation is based on the classifier and get the difference of classification performance. 2. At the same time the height and weight data as the characteristic, with Fisher identification method for linear classifier, and the application of the classifier to training and test sample, investigation and test training error condition. Will the training sample and utility of decision on the drawing boundary paint, and at the same time before the Bayes method with optimal classifier also paint chart, the result of the comparison of the similarities and differences. 3. Select the above or before any a kind of method of experiment, with a method for training set in the estimation error rates, and on the test set get error rates are compared.)

学习APF过程中的读书笔记和MATLAB仿真模型(Reading notes and MATLAB simulation models in learning APF process)

matlab优化方法 不精确的一维线性搜索(matlab optimization method inaccurate one-dimensional linear search)

A discrete cosine transform (DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. DCTs are important to numerous applications in science and engineering, lossy compression of audio (e.g. MP3) and images (e.g. JPEG) (where small high-frequency components can be discarded), to spectral methods for the numerical solution of partial differential equations. The use of cosine rather than sine functions is critical for compression, since it turns out (as described below) that fewer cosine functions are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions.

说明：  ESPRIT 算法的MATLAB仿真程序(ESPRIT algorithm MATLAB simulation program)