erciyangtiao

这是一个基于线性矩阵的神经网络控制程序，它比普通的更简单，因为其应用的参数较少。(This is a learning model for matrix-based data. It can be more efficient than standard vector-based models when the training sample is small or the inputs are matrices, since this model has less parameters than standard ones and exploits an inner structure of the inputs)

4g technologies LTE WIMAX

matlab 可视化 微分方程 物理 算法 超越方程 科学计算 数学变换 最值问题 图像增强 图形界面 线性方程 元胞自动机 晶体生长 金融(physical algorithm matlab visualization in scientific computing differential equations of mathematical transformations beyond the most value problem of linear equations graphical image enhancement of cellular automata Crystal Growth Finance)

《simulation and software radio for mobile communication》这本书写得挺好的，这书上的配套源代码，包括通信各个模块，并对各个模块又完整的程序和详细的程序说明，相信会有你需要的()

自适应，arma ，gm ，都是根据论文写得，结果和论文一样，仅供参考。(Adaptive, arma, gm, are based on written papers, and papers as a result, are for reference only.)

这是一个matlab编写的关于图形图像处理的库文件,里面含有很多实现的图形图像处理的算法,可以使操作者方便使用(This is a matlab graphical images prepared by the treatment of the library file, which contains many realize the graphic image processing algorithms, allows the operator easy-to-use)

包括4天线准正交码的编码和解码程序，其中解码程序包括部分行的合并解码(Including 4 antenna quasi-orthogonal code encoding and decoding procedures, which include decoding part decoding the combined line)

matlab讲义，附带部分源代码，是一份不错的学习matlab资料，哪个学校的忘记了。(matlab handouts, incidental part of the source code, is a good study matlab data, which schools have forgotten.)

K均值聚类算法,matlab编写,很好用(K means clustering algorithm, matlab prepared very well with)

这是模式识别中最小错误率Bayes分类器设计方案。 自行完善了在不同先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。 调用最小错误率贝叶斯分类器决策子函数时，根据先验概率数组，通过比较概率大小判断一个体重身高二维向量代表的人是男是女。 主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小错误率贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到不同先验概率条件下错误率的统计。 (This is the minimum error rate pattern recognition Bayes classifier design. Self- improvement prior probability in different conditions , male , female and total error rate error rate statistics , into which each array . All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions . Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector third step is to application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function . Call the minimum error rate decision Functions Bayesian)