imnoise3.m

MATLAB函数大全_A-Z，可以看看，很不错的(MATLAB function Daquan _AZ, very good)

这是MATLAB的统计工具箱的应用，数模培训的必要内容(This is the MATLAB statistical toolbox of applications, several essential elements in training mode)

非常好的--/---非常全/---数学手册/2007版(Mathematical manual/2007)

针对matlab中simulink输出图动态放大镜(For matlab simulink outpu )

利用matlab实现三次样条插值。包括实验报告和代码(use matlab to realize spline)

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)

matlab数值计算，入门基础，绝对好用(matlab numerical calculation, entry-foundation, absolutely easy to use)

This paper presents a new and efficient approach for capacitor placement in radial distribution systems (RDS) that determine the optimal locations and size of capacitor with an objective of improving the voltage profile and reduction of power loss. The solution methodology has two parts: in part one the loss sensitivity factors (LSF) are used to select the candidate locations for the capacitor placement and in part two a new algorithm that employs Dijkstra Algorithm (DA) is used to estimate the optimal size of capacitors at the optimal buses determined in part one. The main advantage of the proposed method is that it does not require any external control parameters. The other advantage is that it handles the objective function and the constraints separately. The proposed method is applied to 33 & 69 RDS. The solutions obtained by the proposed method are compared with other methods. The proposed method has outperformed the other methods in terms of the quality of solution. OPTIMAL C

Simulacion sistemas de control PID

To reduce the piracy .....here we use a watermark technique...