nprot.2011.424-S5

基于帐篷定理的计算矩形巷道中接受点功率大小的仿真(Theorem based on the calculation of rectangular tunnel tent to accept the size of the simulation point of power)

calculates the frft for a cosine signal for different angles and plots them as the program is running

受电弓激振实验程序，如果学习相关课程的同学很有可能会用得到这个程序，很有用。(Pantograph exciting experimental procedure, if learning courses students are likely to use to get this program useful.)

LMI basics ,slides tutorial

用二维热传导分数步长法求解二维热传导方程，其中解转化来的方程用的是追赶法,分别用了ADI,LOD,对称LOD方法进行求解(Solving the two-dimensional heat conduction equation with two-dimensional heat conduction fractional step method, Solving the two-dimensional heat conduction equation with two-dimensional heat conduction fractional step method, which transformed to the equation solution is to catch up with the law, respectively, with the ADI, LOD, LOD method for solving symmetric)

pdf格式的,由张志涌编写.我在网上找了很久才找着,特推荐给大家下载.(pdf format, prepared by Zhang Zhiyong. I am looking for a long time before the Internet find, especially recommended to everyone to download.)

基于matlab牛顿-拉夫逊 无功补偿优化设计(Newton- Ralph reactive power compensation optimization design)

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)

Code in matlab about processing signals

Bacteria foraging algorithm for optimisation of anything...