funcwin

tsp问题 一些经典的MATLAB程序 模拟退火算法 遗传算法 望高手指几点(tsp)

数字基带系统MATLAB仿真程序，符合专业设计要求(DIGITAL BASEBAND MATLAB CODE)

hystrysis simulink model

Designs a linear phase FIR lowpass filter such that it: - minimizes maximum stopband attenuation - has a constraint on the maximum passband ripple This is a convex problem (when sampled it can be represented as an LP). minimize max |H(w)| for w in the stopband s.t. 1/delta <= |H(w)| <= delta for w in the passband ( where H is the frequency response function and variable is h (the filter impulse response). delta is allowed passband ripple. )

一种基于调制域分析的过零检测算法改进，克服了传统过零算法不能应用于某些信号的局限，有MATLAB仿真(Zero-crossing detection algorithm based on improved modulation domain analysis, to overcome the traditional limitations of the zero crossing algorithm can not be applied to certain signals, there are MATLAB simulation)

Three wire unbalanced load....

有关频率调制的实例，简洁实用，很有针对性，已经运行通过(For examples of frequency modulation, simple and practical, very targeted, has been running through the)

模型预测控制非常基础的程序，里面的注释也很全面(model predictive control)

The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order (The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order)

稀疏建模工具箱，是一个为解决各种稀疏估计问题的开源优化工具箱，可实现字典学习与矩阵分解；使用LARS、快速下降法、OMP、SOMP等算法解决稀疏分解问题；使用Lasso解决结构化稀疏分解问题( Dictionary learning and matrix factorization Solving sparse decomposition problems with LARS, coordinate descent, OMP, SOMP, proximal methods Solving structured sparse decomposition problems )