plot_Time_series_of_MCA

This code is an implematation of AIRS1 algorithm. I hope this is helpful.

converter control pwm simuling

发射信号为线形调频信号雷达的计算机仿真!(fired linear FM signal to the radar signal computer simulation!)

wind turbine simulation

无桥boostPFC的单周控制，参数可调，模型基本正确(No bridge boostPFC single cycle control, adjustable parameters, the model essentially correct)

自己用matlab写的算术编码的程序，信源为二元信源,信源序列、码长均可自定义(own use Matlab write arithmetic coding procedures, source of the dual source, the source sequence, code length can be defined)

BCH码编码译码方法研究及仿真，主要有n-k编码、代数编码、彼得森译码、BM译码、群变换译码等，用MATLAB仿真。(BCH codes encoding&decoding methods and simulation)

使用大量的有限元法求解偏微分方程，是国外的成品模型，是本科毕设的题目，能量熵的计算，GPS和INS组合导航程序，利用自然梯度算法。( Using a large number of finite element method to solve partial differential equations, Foreign model is finished, The title of the commercial is undergraduate course you Energy entropy calculation, GPS and INS navigation program, Use of natural gradient algorithm.)

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...)

说明：  基于粒子群的算法和混沌搜索的协同优化算法(Collaborative optimization algorithm based on particle swarm algorithm and chaotic search)