BLDCM

一篇描述非线性滤波的经典文章，推荐给大家，文中有程序的介绍。(a description of the classical nonlinear filtering articles, recommended to you the text of the procedures introduced.)

无刷直流电机控制系统的研究印度的老外编的(studiedof the Brushless dc motor control system )

filter design sigma delta filter(filter design sigma delta filter)

说明：  这是个混沌理论应用中的李雅普诺夫指数源代码，里面的方程是著名的lorenze方程，它可以帮助我们分析混沌的动力学特性，希望对大家有用(This is application of chaos theory in the Lyapunov index of the source code, which the equation is the famous lorenze equation, it can help us analyze chaotic dynamics, we hope to be useful)

Matlab/Sinulink PI控制的Boost升压电路(Matlab/Sinulink PI Control Boost cricuit)

MIMO-OFDM发射机的labview编程实现(Labview programming implementation of MIMO-OFDM transmitter)

KFILTER can have arguments: (A,C,V1,V2) if there are no cross

飞机大角度攻击的模型仿真程序，描述了在大角度攻击时的姿态控制参数的计算。(Model for large angle of attack aircraft simulation program, described the attack in the large angle attitude control parameter calculation.)

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...)

journal with respect to research work with some related work on routing in networks.