Matlab_VC_hybrid-programe

matlab exercise hope can be useful

Matlab教程第7章，编程与数据分析、整理(Matlab Guide Chapter 7, programming and data analysis, sorting)

基于MATLAB的数字水印程序，我不懂大家下着看吧(MATLAB-based digital watermarking process, I do not know how we look forward to the next)

kalman滤波在嵌入式中的应用，可用于智能车的制作，对陀螺仪和加速度计进行优化。(kalman filtering applications in embedded in can be used smart car production, gyroscopes and accelerometers for optimization.)

在简要介绍 Matlab软件的基础上 ,重点分析了 Matlab和 V B混合编程的方法讨论(This paper introduces Matlab software, analyzes applications of Matlab and VB programming with signal analysis and processing)

四轴飞行器Matlab仿真分析，涉及四轴飞行器主要部件如Mottor，camera，电池等，分析包含稳定性、动力等 已通过测试。 (VRML simulation on the real life in a common environment (multimedia classroom), very often realistic image, the code is very long, but it is easier to understand. Has been tested.)

费歇算法 用于模式识别等其他　工程应用方面的　问题　应用效果　良好(Fisher algorithm for pattern recognition and other aspects of engineering applications a good application effect)

install code to the document,and replease ,make project

粒子群优化算法（PSO）是一种进化计算技术（evolutionary computation）,由Eberhart博士和kennedy博士于1995年提出 (Kennedy J，Eberhart R． Particle swarm optimization．Proceedings of the IEEE International Conference on Neural Networks．1995．1942~1948.)。源于对鸟群捕食的行为研究。粒子群优化算法的基本思想是通过群体中个体之间的协作和信息共享来寻找最优解． (Particle Swarm Optimization (PSO) is an evolutionary computation techniques (evolutionary co mputation) by Dr. Eberhart and kennedy Dr. raised in 1995 (Kennedy, J., Eberhart R. Particle swarm optimization.Proc eedings of the IEEE International Conference o n Neural Networks.1995.1942 ~ 1948.) . From the flock of the predatory behavior. PSO algorithm is the basic idea of individual groups through the sharing of information and collaboration to find the optimal solution.)

This derivation of the normalised least mean square algorithm is based on Farhang- Boroujeny 1999, pp.172-175, and Diniz 1997, pp 150-3. To derive the NLMS algorithm we consider the standard LMS recursion, for which we select a variable step size parameter, μ(n). This parameter is selected so that the error value , e+(n), will be