m1

separation nonlineaire de sources par

运用奇异值分解，离散傅里叶变换的水印算法，完整程序(wartermarking algorithm)

说明：  基于GA改进BP神经网络的预测算法，可用(Forecasting algorithm based on GA improved BP neural network, available)

Another particle filter implementation (by by Diego Andrés Alvarez Marín) that implements Arulampalam et. al. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing. 50 (2). p 174--188

MATLAB CODE FOR BEM

matlab编写的文件名批量修改程序，比较实用，可是试试看(matlab batch file name written in the amendment procedure, more practical, but give it a try)

SIMULATING THE LONG TERM EVOLUTION PHYSICAL LAYER

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)

使用Matlab計算AHP分析, 特徵值與特徵像量, 計算最佳決策分案選擇(To use the Matlab calculation AHP analysis, Eigenvalues ​ ​ and like, calculate the divisional selection of the best decisions)

Displays todays date and time in Matlab