climatiques

matlab code for clastering

2D Advection Equation Riemann solver

无约束多维极值问题,适合数学计算,优化分析等极值问题的求解(Unconstrained multi-dimensional extremum problem, is suitable for mathematical calculations, the extremum problems of solving optimization analysis, etc )

After this, the robot grasps the spherical light diffuser again employing the time- independent image path tracker proposed here

DC LOAD FLOW TECHNIQUE

非常不错的书籍，《微粒群算法》供8章和2个附录，依次上传第6章绪论(Very good books, Particle Swarm Optimization for 8 chapters and two appendices, followed by From Chapter 6 Introduction)

economic load dispatch

此例程为配套书籍的第六章例题6-10的相对应代码，功能为方波消噪示例，编程环境为matlab。(This routines is for the supporting books of the for Chapter VI Example 6-10 the the relative should be of code, function de-noising of the is a square wave Example, programming environment for the the matlab.)

this is the DCT image compression code i have tried . i missed out 2 files while uploadin last time sorry :) hey guys i missed 2 files

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)