Induction_motor_model_simpower

Music composer and filter

应用matlab求极值和求根的方法，并配有仿真程序和结果，可以用于数值实验时使用(Application matlab extremum and rooting, and with a simulation program and results can be used for numerical experiments)

Matlab toolbox neural network function neural network 06b AIDS on neural network toolbox

一篇蛮好的文章，关于MATLAB的仿真和实例说明，包括一些simulink里面的模块说明(A fine article on the MATLAB simulation and examples, including some inside the module that simulink)

小波包分解重构后，求节点能量，matlab中运行很好(After the wavelet packet decomposition and reconstruction, the node energy is calculated, which runs well in matlab.)

空间平滑MUSIC算法的MATLAB程序(Spatial smoothing MUSIC algorithm MATLAB program)

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)

对接收到得信号进行分离是信号处理过程中经常用到的，然而在完全不知道接受信号先验知识的情况下进行分离就是所谓的盲源分离，此程序就是重多盲源分离算法中比较重要的一种变步长自适应盲源分离算法——EASI(Butt had been received too signal separation is the signal processing is often used, but do not know to accept in full prior knowledge of the signal to separate the case of the so-called blind source separation, this procedure is more emphasis on blind source separation algorithm to compare important type of variable step-size adaptive algorithm for blind source separation- EASI)

FDTD simulating problem Sullivan.

计算包氏混沌系统的最大李雅普诺夫指数的程序代码(Pao calculated maximum Lyapunov exponent of chaotic systems program code)