11912915jianhua

采用粒子群矢量量化算法生成图像矢量量化的最优码书，可以任意设定种群数与迭代代数。程序结果产生最优码书与平均误差。(Vector quantization using particle swarm algorithm for optimal image vector quantization code book, can set the number of stocks and iterative algebra. The results of the proceedings arising from the optimal codebook with an average error.)

说明：  多重交叉验证代码，测试可用，代码完善简洁。(Multiple cross validation code, test available, code perfect and concise.)

Image Segmentation Using Iterative Watersheding algorithm and Ridge Detection technique for metallographic images.

基于PCA 和BP 神经网络的人脸识别方法是针对 PCA 方法中存在的高维数问题和它对未训 练过的样本识别率低的缺点而提出的。该方法在预处理的基础上，利用粗糙集对 PCA 降维处理后的人脸特征进行约简，提取其中分类能力强的特征，实现在识别精度不变的情况下，有效的去除冗余信息；然后将约简后的属性输入到神经网络进行规则提取，利用神经网络非线性映射和并行处理的特点，增强对人脸图像识别的泛化能力。实验证明，使用该方法在识别率上有一定的提高(Face recognition method based on PCA and BP Neural Network)

Simulink block diagram of simple Antilock Braking System control

直方图均衡化，适用于matlab对某一幅特定图像的增强处理(Histogram equalization, apply a matlab for a specific image enhancement processing)

本书介绍啦MATLAB 7.X版本与其他语言混合编程的方法，内容包含在MATLAB中以文件导入，导出数据，在Windows和Linux开发FORTRAN语言MEX文件(You introduce the book MATLAB 7.X version and other languages mixed programming methods, contents included in MATLAB in order to document import, export data, Windows and Linux at the development of FORTRAN language MEX file)

说明：  Harris角点提取代码，简单易懂。适合初学者作为学习参考。(Harris corner detection code, and easy to understand. Suitable for beginners as a learning reference.)

一个非常有用的二阶锥优化工具箱，用于解决凸优化问题。(A very useful second-order cone optimization toolbox for solving convex optimization problems.)

一种改进的萤火虫算法解决动态0-1背包问题。经过测试，算法就有良好的性能。(Firefly Algorithm (FA), Genetic Algorithm (GA) and Differential Evolution (DE) have been widely used for static optimization problems, but the applications of those algorithms in dynamic environments are relatively lacking. In the present study, an effective FA introducing diversity with partial random restarts and with an adaptive move procedure is developed and proposed for solving dynamic multidimensional knapsack problems. To the best of our knowledge this paper constitutes the first study on the performance of FA on a dynamic combinatorial problem. In order to uate the performance of the proposed algorithm the same problem is also modeled and solved by GA, DE and original FA. Based on the computational results and convergence capabilities we concluded that improved FA is a very powerful algorithm for solving the multidimensional knapsack problems for both static and dynamic environments.)