dielectriscatteringPMl

用于计算机计算潮流的仿真运行程序,基于matlab程序编程的程序.(LOAD FLOW)

说明：  Anova analysis for vibration

说明：  通过matlab环境，将rgb24格式的avi文件提取出bmp格式文件并逐帧保存。(transfer RGB24 avi files to bmp images.)

Matlab code for Fuzzy C-Means Segmentation Algorithm

SpaceVectorPWM状态空间矢量控制模型(SpaceVectorPWM)

A MLP code with backpropagation training algorithm designed for aproximation of functions problems.

通訊方面眾多原始碼，包含OFDM.CDMA.STTC.Power Control.Antenna Array方面的原碼，用於調變.解調.編碼.傳送.功率控制等方面。(Source of many communications, including aspects of OFDM.CDMA.STTC.Power Control.Antenna Array original code used for modulation. Demodulator. Coding. Transmission. Power control and so on.)

emd经验模态分解 对振动信号进行emd分解后可以得到若干个imf,然后便可以进行希尔伯特黄变换，判断故障。(emd, it is used to analyse a signal)

MATLAB est un logiciel de calcul matriciel à syntaxe simple. Avec ses fonctions spécialisées, MATLAB peut être aussi considéré comme un langage de programmation adapté pour les problèmes scientifiques

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)