MTDC

非常简单明了的基本谱减法降噪Matlab实现。 代码内有详细的注释。适合初学者。(An normal spectral subtration algorithm write by matlab.)

this is simple genetic algorithm code

Adaptive Neuro-Fuzzy Inference System (ANFIS) (based on Fuzzy Logic Toolbox. User’s Guide.)

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)

IBMde股票图形示例，，含数据有布林线，平均移动线 (IBMde stock graphic example, with data Bollinger Bands, Moving Average Line)

学习matlab的资料，包含matlab的练习题和上机训练(Learn matlab materials, including Matlab exercises and computer training)

[matlab] DIP nearest neighbor interpolation and bilinear interploation ([matlab] DIP nearest neighbor interpolation and bilinear interploation)

一个本科毕业设计，关键是有源码。内容为ofdm系统仿真，包括设计方案，仿真参数设置等。(A graduation project, the key is a source. Content ofdm system simulation, including the design, simulation and parameter setting.)

FCM算法的matlab实现，经典的FCM算法，使用IRIS数据(FCM algorithm matlab realize, classic FCM algorithm, the use of IRIS data)

Function to perform Principle Component Analysis over a set of training vectors passed as a concatenated matrix. Usage:- [V,D,M] = pca(X,n) [V,D] = pca(X,aM,n) where:- <input> X = concatenated set of column vectors aM = assume that the mean is aM n = number of principal components to extract (optional) <output> V = ensemble of column eigen-vectors D = vector of eigen-values M = mean of X (optional)