data_acquisition_Simulink

the comb pilot aided channel estimation in ofdm modulation is considered. the power of pilot subchannels is under the control. The data experience frequency selective channel then the AWGN noise is added to make the situation worse. The bit error rate is ploted versus SNR.

一个关于Matlab数学工具软件实例简明教程的电子文档(A mathematical tool Matlab instances on Concise Guide to the Electronic Document)

有关于自然梯度算法的matlab源程序，非常好用。希望对大家有用(about the NG matlab blind source separation is very cool got it!)

偏微分方程有限元的数值解的matlab程序，具有较好的实用性，方便大家使用和参考。(Partial differential equations finite element numerical solution matlab program, with good usability, to facilitate the use and reference.)

moore响度模型erb级计算，完成版，共同没努力(moore loudness calculation)

这是matlab编程的一个例子，编写M文件，实现正弦摆动的一个动画。(Matlab is a programming example, the preparation of documents M to achieve a sinusoidal oscillation of animation.)

GSO算法及其改进，其中HGSO是通过与和声搜索算法结合而成，主要用于结构的截面优化、几何优化、动力优化、拓扑优化等，具有收敛速度快，收敛结果好的优点，且其改进算法能摆脱局部最优的缺陷。(GSO algorithm and its improvements, HGSO is through a combination of harmony search algorithm, mainly used to optimize the structure of the cross section, geometry optimization, dynamic optimization, topology optimization, a fast convergence, convergence results is good, and its improved algorithm defects can get rid of local optimum.)

时间序列分析，AR模型，用于流数据预测与滤波 输入参数：y为原始数据矩阵，p为AR模型的阶数，la为自回归模型的遗忘系数 输出参数：预测值，置信区间，离群点等(Time series analysis, AR model for prediction and filtering data stream input parameters: y original data matrix, p is the order of the AR model, la self-forgetting coefficient regression model output parameters: predictive value, confidence intervals, outlier points)

Product table for numbers in matlab

传统的K-medoids聚类算法的聚类结果随初始中心点的 不同而波动，且计算复杂度较高不适宜处理大规模数据集； 快速K-medoids聚类算法通过选择合适的初始聚类中心改进 了传统K-medoids聚类算法，但是快速K-medoids聚类算法 的初始聚类中心有可能位于同一类簇。为了克服传统的K- medoids聚类算法和快速K-medoids聚类算法的缺陷，提出 一种基于粒计算的K-medoids聚类算法。(The traditional K-medoids clustering algorithm clustering results with different initial center points and volatility, and high computational complexity is not suitable for processing large data sets; K-medoids clustering algorithm by choosing proper initial cluster centers to improve the traditional K-medoids clustering algorithm, but the initial cluster center of K-medoids clustering algorithm can be located in the same cluster. In order to overcome the shortcomings of the traditional K- medoids clustering algorithm and the fast K-medoids clustering algorithm, a K-medoids clustering algorithm based on granular computing is proposed.)