felogit

提供一种求解最优哈密尔顿的算法---三边交换调整法，要求在运行jiaohuan3（三交换法）之前，给定邻接矩阵C和节点个数N，结果路径存放于R中。 bianquan.m文件给出了一个参数实例，可在命令窗口中输入bianquan,得到邻接矩阵C和节点个数N以及一个任意给出的路径R,，回车后再输入jiaohuan3,得到了最优解。 由于没有经过大量的实验，又是近似算法，对于网络比较复杂的情况，可以尝试多运行几次jiaohuan3,看是否能到进一步的优化结果。 (Provide an optimal algorithm Hamilton- trilateral exchange Adjustment Act, requiring running jiaohuan3 (c exchange) before a given adjacency matrix C and the node number N, the results of the path stored in the R in. bianquan.m document gives examples of a parameter can be in the command window, type bianquan, be the adjacency matrix C and the node number N and an arbitrary given path R,, carriage return after the input jiaohuan3, have been the optimal solution . After the absence of a large number of experiments, it is approximate algorithm, for more complex network situations, you can try to run several jiaohuan3, can look to further optimize the results.)

在Matlab里导入.cnt文件，主要帮助在Matlab中进行EEG脑电分析的同学，将.cnt文件成功导入Matlab，然后分析数据结构。(import .cnt file in matlab)

关于跳频信号的产生，调制和解调，可自行设置几跳，内有m序列的产生等调用函数，适合初学者参考。。(The generation of hopping signal, including modulation and demodulation, you can set a few jump, there were the funciton of m sequence and so on. It is suitable for beginners .)

用来求PST问题，聚类分析的一些重要的复杂的实际性方法等等。(POST is very good way of solving simple matter)

simlink的开发和框图还有s文件通过它可跟好的了解simlink的使用(Block Diagram of the development and simlink)

0_Linear：线性回归练习 1_LogistRegression：逻辑回归练习 2_boston：波士顿房价预测 3_regularization：正则化 5_stock：股票价格预测 6_CTR：点击率预测 7_all_model：回归与分类模型 8_muticlass：多分类练习 9_regression_implement：不同回归算法tf实现(0_Linear. 1_LogistRegression. 2_boston. 3_regularization. 5_stock. 6_CTR. 7_all_model. 8_muticlass. 9_regression_implement.)

本程序是线阵的波束形成程序，可以在特定方向生成零陷，从而抑制干扰信号(This procedure is a linear array of beam forming process, you can generate zero in a specific direction, thereby suppressing the interference signal )

hall effect current sensor

最小生成树kruskal原程序——根据数据结构最小生成树的邻接矩阵( Minimum Spanning Tree kruskal of the original program- the data structure of the adjacency matrix of the minimum spanning tree)

基于多元线性回归、偏最小二乘、神经网络、卡尔漫滤波、径向基网络、主成分分析等等的程序。可用于建模和预测。(Based on multiple linear regression, partial least squares, neural networks, Carl diffuse filtering, radial basis networks, and so on principal component analysis procedure. Can be used for modeling and prediction.)