PV_modele3

it is a programm that simulate mossaic effect on the field of communicatons under AWGN noise

用MATLAB语言求解杆的运动，采用线性插值。(Using MATLAB to Solve the rod movement, using linear interpolation.)

现代数字信号处理 ESPRIT算法仿真 (single and system ，matlab ESPRIT)

garchfit函数的用法，用于ARMA参数的拟合，给出参数的极大似然估计值。(The function of the usage of garchfit for ARMA parameter fitting, given the parameters of a great likelihood estimate.)

主要用来进行MATLAB中AWGN的识别算法!(mainly used for MATLAB AWGN identification algorithm!)

具有相关性的风速序列的拉丁超立方采样方法的例子，包含Cholesky以及排序(Examples of correlation between wind speed sequence of Latin hypercube sampling method, including Cholesky and sorting)

RADAR RADIATION PATTERN, Transmit-mode Radiation Pattern, Doppler Spectrum of Transmit-mode Radiation Pattern, Doppler Spectrum of Transmit-Receive Mode Radiation Pattern

三个图像压缩matlab源代码，希望大家喜欢(Three image compression matlab source code, hope you like)

matlab数字信号处理实例；MATLAB下的数字信号处理实现示例(matlab digital signal processing examples MATLAB under the sample implementation of digital signal processing)

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)