pingfanglvjianbo

贝叶斯分类器的设计与实现，非常好的应用程序，能够在其上面实现人脸识别(Bayesian classifier design and implementation of a very good application, face recognition can be achieved in the above)

数据关联是多目标跟踪的一项关键技术。JPDA是大家公认的多目标跟踪中性能较好的数据关联算法，它 认为量测和目标是一一对应的关联关系，但在许多实际情况中，量测和目标是多一多对应的关系。针对上述情况，该文提 出了广义概率数据关联算法(Generalized Probability Data Association，GPDA)。文中从理论上对这两种算法的性能进行了 详细分析，并利用Monte Carlo技术对其性能进行了仿真比较。(Data association is one of the key technologies in multi—target tracking．And JPDA is considered as the best da· ta association method．JPDA considers the association of measurements with targets is simply one-to-one problem．But in many practical cases，the association of measurements with targets will be multiple—to—multiple problem．For this case，a Generalized Probability Data Association(GPDA)algorithm is proposed in this paper．Furthermore，this paper analyzes the performance of these two algorithms theoretically．And we give the comparative analysis of those performances by using Monte Carlo method．)

认知无线电能连检测法Matlab仿真 此程序检测了认知无线电能量法的性能 并绘图(CR Energy Detection matlab)

运用hurst指数检验水文时间序列是否具有趋势性（单组数列&多组数列矩阵形式）(Use hurst index test whether hydrological time series trend (single-set number of columns & multiple columns in matrix form))

现代通信系统（matlab版）（第二版）配套源代码 John G. Proakis等著 刘树棠 译 电子工业出版社出版(modern communication system (Matlab version) (2nd Edition) source code supporting John G. Proak Liushutang is waiting for the translation of the electronics industry published)

基于LBP纹理特征的图像表达！采用MATLAB编程语言实现。(LBP coding。)

模糊神经网络的预测算法,嘉陵江水质评价,MATLAB的经典算法(Fuzzy neural network prediction algorithm, Jialing River water quality assessment, MATLAB classical algorithm)

基于matlab的关于遗传算法的一些重要的函数的m文件，供读者参考 (Based on some of the important matlab m file on genetic algorithm function for readers reference)

说明：  用于工业机器人轨迹规划，主要是蚁群算法的matlab实现。(For trajectory planning of industrial robots.)

线性回归算法,其功能是对一组数据进行分析预测,数据分为年度,月度,季度数据(Linear regression algorithm, and its function is to a group of data analysis and forecasting, data is divided into annual, monthly, quarterly data)