基于改进遗传算法的路径规划MATLAB实现

基于小线变换的多尺度几何分析方法.很好的论文(xiaobo)

Matlab矩阵以及数组等的基础训练题目(Matlab matrix as well as an array of basic training topic)

抗jsteg的隐写分析，jsteg jpeg图像隐写的重要方法之一。(Anti-jsteg of steganalysis, jsteg jpeg image steganography one of the important ways.)

MATlab避障控制算法 需要安装Tomlab工具箱，比matlab自带工具箱更具有优点！(MATlab obstacle avoidance control algorithm will need to install Tomlab toolbox, matlab own toolbox more than the benefits!)

这个源码提供了穿墙雷达反向投投影成像算法的实现 (This source provides a through-wall radar projectors imaging algorithm to achieve reverse)

很经典的一个多核学习程序SimpleMKL，包括对应的文章和demo程序，可以很容易学习和上手MKL的原理和应用！(A classic learning program multicore SimpleMKL, including the corresponding articles and demo program that makes it easy to learn and to use the principles and applications of MKL!)

HVDC Increasing Popularity

说明：  《计量地理学》（徐建华，高等教育出版社，2005）配套实习指导,利用Matlab编程计算非线性回归模型,以Logistic曲线为例( Quantitative geography (Xu Jianhua, Higher Education Press, 2005) supporting practice guidance, the use of Matlab programming non-linear regression model calculated to Logistic curve as an example)

多个电力电子、电机仿真模型。是重要的学习电力电子simulink 仿真的素材，权威教材，内容较多，分成上下两集,这个是上(useful simulink modles for leaners)

Rosenbrock函数优化属于无约束函数优化问题，其全局极小值位于一条平滑而狭长的抛物线形状的山谷底部，且为优化算法提供的信息很少，因此找到其全局极小值就显得很困难。根据Rosenbrock函数的这种特性，专门提出了一种改进的PSO算法，该算法引入三角函数因子，利用三角函数具有的周期振荡性，使每个粒子获得较强的振荡性，扩大每个粒子的搜索空间，引导粒子向全局极小值附近靠近，避免算法过早地收敛，陷入局部最优，从而找到Rosenbrock函数的全局极小值。大量实验结果表明，该算法具有很好的优化性能，为某些领域某些特定的类似于Rosenbrock函数的优化问题提供了一种新的思路。(Rosenbrock function optimization functions are unconstrained optimization problems, its global minimum value at a smooth parabolic shape and narrow mountain valley Department, and for the optimization algorithm provides little information, and therefore find its global minimum value becomes very difficult. According to this feature Rosenbrock function, specifically mentioning Out an improved PSO algorithm (PSO-R), the algorithm introduced trigonometric factor, using trigonometric has periodic oscillations, so that each particle to obtain more Strong oscillations of expanding the search space of each particle, the particle is close to nearby guide global minimum, avoiding premature convergence algorithm, into a local optimum, thus Finding the global minimum Rosenbrock function. Experimental results show that the algorithm has a good optimizing performance, similar to some certain areas Rosenbrock function in the optimization problem provides a new way of thinking.)