Swarm-Tracking

全英文，讲述matlab功能，使用，开发等等(Wholly in English, on the matlab function, use and development and so on)

说明：  GP算法计算嵌入维数，包括了关联维数的程序等等都集合在里面 可以看到源程序，加入了典型混沌数据供各位同行进行分析。(GP algorithm embedding dimension, including the correlation dimension of the program, etc., which can be seen in the collections of the source, adding the typical chaos for colleagues to analyze the data.)

modify newton codes...........

用mathlab解关于卫星轨迹图的微分方程组, 简单实用的例子,能从中知道如何编写程序, 并如何画图.可直接在matlab7.0中运行. 以及有关于复杂网络的matlab源程序，供致力于复杂网络仿真研究的同仁参考！(Appollo satellite s trace (Differential equation))

用于模拟电磁波在光子晶体中的传播，使用平面波展开法，利用完美耦合的吸收介质层以提高计算效率。(Used to simulate the electromagnetic wave propagation in photonic crystals using plane wave expansion method using perfect coupling absorbing medium layer to improve computational efficiency)

快速度模型预测控制的C和matlab程序，希望和大家分享(fast MPC C and Matlab program)

MLFMA在运动目标多普勒回波数值仿真中的应用(MLFMA applications moving target Doppler echo numerical simulation)

This is an MMC model for matlab

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.)

说明：  基于蚁群算法的边缘检测，功能简单，适合新手(Edge detection based on ant colony algorithm, the function is simple, suitable for beginners)