VCSOA_Bistability_RateEquations

一个描述奇异值分解的matlab小程序，通俗易懂，方便移植。(A description of the singular value decomposition matlab small program, easy to understand, easy to transplant.)

music算法的matlab实现，可以直接运行(music algorithm matlab implementation can be directly run)

用蝙蝠算法（BA）对极限学习机（ELM）的输入权值和偏执进行进行优化，其诊断精度有明显提升。(With bat algorithm (BA) on Extreme Learning Machine (ELM) input weights and paranoia were optimized diagnostic accuracy has improved significantly.)

一种非线性主成分分析Matlab代码 对数中心化处里(A nonlinear principal component analysis Matlab code number in the center of the Department of)

This histogram is a graph showing the number of pixels in an image at each different intensity value found in that image For an 8 bit grayscale image there are 256 different possible intensities , and so the histogram will graphically displays 256 numbers showing the distribution of pixels amongst these grayscale values

LDPC maker matrix matlab code

基于labview时频分析，在线故障诊断(for labview)

采用GA对神经网络的权值与阀值进行优化，得到效果更好的网络(GA uses neural networks for weights and thresholds are optimized to get better network)

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)

严格按照BP网络计算公式来设计的一个matlab程序,对BP网络进行了优化设计(Neural Network——BP)