Colorsegmentation

彩色电视机接收系统仿真，功能模块完整，适合初学者，建议参考(Color TV receiver system simulation, complete modules, suitable for beginners, it is suggested)

a pre-processing box for standard Microarrays data set

给文件包含了几乎所有数学建模用到的分析法，如模糊数学，神经网络，灰色模型，并包含有相关模型matlab代码，可作为建模竞赛培训材料。(To the file contains almost all of the mathematical modeling used in the analysis, such as fuzzy, neural network, the gray model and includes the relevant model matlab code can be used as training materials modeling contest.)

这是发表在science上的关于聚类的文章 论文后面附者算法实现过程，其上的网址有matlab程序(This is published in the science on the cluster of articles on the papers attached to those behind the algorithm process, the Web site have matlab program)

sareli<matlab科学计算>配套程序(sareli )

用MATLAB语言实现BCH的编码和译码(BCHcode and decode)

Pulse shaping using raise cosine filter for QPSK

北斗二代B1频点接收机主程序Matlab源代码(BD2B1 Matalab Receiver)

Iris recognition using Support vector machine and Hamming distance

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)