DTC-neurofuzzy

说明：  欧洲winner计划里建立的MIMO 空间信道模型。可用于MIMO仿真。(The European winner in the establishment of MIMO space program channel model. Simulation can be used for MIMO.)

说明：  复杂网络BA模型matlab代码，返回网络图的邻接矩阵，代码中采用稀疏矩阵，极大地释放内存，提高效率。(BA model complex network matlab code, return to the network adjacency matrix, the code used in sparse matrix, which greatly release memory and improve efficiency.)

说明：  强力分水岭，一种融合图割，随机游走，最小路径的基于图论的分割算法(Strong watershed, a fusion graph cuts, random walks, the minimum path segmentation algorithm based on graph theory)

本程序对简约的粗糙集程序进行了matlab编导，从而能够计算粗糙集的权重、约简等(This procedure is simple rough set procedures matlab director thus able to calculate the weight of rough set reduction)

说明：  matlab task by me and friend

MATLAB初级学习和应用以及进一步提高的PPT和源代码(MATLAB primary learning and applications, and to further improve the PPT and source code)

高速铁路路基沉降是一个复杂的系统过程,涉及不同时期、不同工况,选用单一的预测模型不足以反映路基整体的变形规律.Logistic模型及Verhulst模型的曲线均为S形,可以反映全过程的地基沉降与时间的关系.针对路基填筑期和降水期两个时段利用MATLAB软件编制程序计算模型参数,得到Logistic模型和灰色Verhulst模型,进而求得两者的最优加权组合模型.对比结果表明:在路基填筑期由两者的最优加权组合模型预测得到的结果与实测结果较吻合,拟合精度高于单一模型 而在降水期Verhulst模型的预测值更接近实测值,平均相对误差约为1.75％,拟合精度较高.(High-speed railway subgrade settlement process is a complex system involving different times and in different conditions, the choice of a single forecasting model does not adequately reflect the overall deformation of roadbed. Curve Logistic model and Verhulst models are S-shaped, the whole process can be reflected foundation settlement versus time. against roadbed construction period of two hours and precipitation using MATLAB software programming model parameters, the Logistic model and gray Verhulst model, and then get the optimal model of a weighted combination of the two. comparative results indicate : results and experimental results in the roadbed construction period by the weighted combination of the best of both models predicted than the anastomosis fitting accuracy than a single model precipitation predicted value of Verhulst model closer to the actual value of the average relative error approximately 1.75 fitting accuracy.)

粗糙集属性约简，包括正域的计算，约简结果的提取(Rough Set Attribute Reduction, including the calculation of positive fields, the extraction of reduction results)

说明：   互信息：计算两幅图像之间的互信息(Mutual information: calculating the mutual information between two images)

原创，使用matlab对激发极化的极化率进行时间谱反演。采用GUI编程，非常方便。(Original use matlab polarization excitation polarization inversion time. GUI programming is very convenient.)