MATLAB-examples

matlab programs used to calculate conv,autocorrelation,dft, idft,etc

雷达试验具体方法，包括线形跳频信号的处理，运动目标的速度测量等(radar specific test methods, including linear frequency signal processing, the speed of moving targets, such as measurement)

为工程设计人员提供帮助，同时为其它设计提供思路(guide)

计盒分形维数计算的matlab源代码编程实现过程(Function [Fdgen NoisfreFD ]=fractaldim(y,Q,M) __________________________________________________________________________ Usage: Computes fractal dimension by box counting(BC) method. NOTE: Running the code may take a little time, because it calculates dimensions for all embedding dimensions up to M. This code is based on an algorithm that constructs a box for the first observation and for other observations test which it belongs to previous box(s). If the observation belongs to one of the existent boxes then increases number of points in the relevant box,otherwise makes a new box. the algorithm of this code uses only rounding down and it does not need binary coding, sorting and so on. It uses only transformation data to [0,2^32-1] and finding valid boxes based on Leibovich and Toth(1989). Inputs: y is a vector time series. q stands for generalized dimension q=1 entropy dimension,q=2 correlation dimension and so on. q=0 is box counting dimen)

采用MATLAB语言编译的数值积分函数，希望对大家有用(numerical integration)

simulation for cost 207 simulation

MATLAB_Demystified matlab揭秘(MATLAB_Demystified)

conventional inverse OFDM for LTE-A

船舶操纵运动仿真模型之风力模型研究（根据经验公式得出）(Research ship motion simulation model of the wind model manipulation (based on empirical formulas derived))

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)