SmithChart

还原性，能够还原色彩的真实程度和评价我们的的光源(reflection)

我导师写的《自适应控制与理论》的源代码。非常好。(My tutor had written the adaptive control and the theory of source code. Very good.)

MATLAB code for learning purpose

基于MATLAB的PQ分解发计算电力系统潮流(MATLAB and PQ decomposition based on power flow calculation)

利用延时来估计单个声源位置，并给出了改进方法和改进以后的一些参数比较。(The use of delay to estimate the location of a single sound source, and give an improved method and improved some of the parameters after comparison.)

一款埃及学生用Matlab编的的基于PSO的PID整定程序，使用方法详见程序注释。(Tunning of PID controller using Bacterial Foraging Orientec by Particle swarm optimization My work has been accpepted in GECCO 2008 as Graduat Student workshop. I have used this techique in PID tunning and i got better result thatn BG and PSO Author: Wael Mansour (wael192@yahoo.com) MSc Student, Electrical Enginering Dept, Faculty of Engineering Cairo University, Egypt)

说明：  :针对遗传算法易陷入局部最优的不足,在标准遗传算法基础上加入了三个新的操作- 复原、重构和录优操作,使改 进后的遗传算法收敛于全局最优,并在此基础上以路边约束、动态避障和路径最短作为适应度函数,提出了动态避障的路径 规划方法。通过实验仿真验证了算法的有效性、准确性和实时性,并与基于以往的遗传算法的路径规划方法进行比较,结果 表明本文提出的方法在产生的路径长度和算法运行时间上都具有更优的性能。(: Genetic algorithm for the lack of easy to fall into local optimum, in the standard genetic algorithm based on the insertion of three new operations- rehabilitation, reconstruction, and excellent operational record, so that the improved genetic algorithm converges to the global optimum, and in this by the roadside on the basis of constraints, dynamic obstacle avoidance and the shortest path as the fitness function is proposed dynamic obstacle avoidance path planning method. Simulation results through experiments the effectiveness of the algorithm, accuracy and real-time, and with previous genetic algorithm based path planning method to compare the results show that the method proposed in the resulting path length and algorithms have a running time better performance.)

说明：  主要都是 MATLAB RBF网络算法，可以直接使用(There are thirteen chapters in this paper. They are mainly intelligent algorithms of MATLAB, which can be used directly.)

The first scheme is based on the spatial locality of feature vectors corresponding to similar images. Learning is effected by modifying the query vector to incorporate the positive examples. The second scheme is based on “distorting” our view of the feature space. An new similarity distance between an image and the query is learned from the relevance feedback.

使用k均值算法对多个数据集进行分类，已测试通过，简单易懂(classification to many datasets using k-means algorithm ,simple and easy-understanding.)