dsp

说明：  混沌时间序列预测程序,包括多步预测函数及多步预报的主程序。(Chaotic time series prediction procedures, including multi-step prediction function and multi-step prediction of the main program.)

蕞优分组周游路线问题是一类物流智能调度问题，是众多的计算难度极大的组合爆炸问题之一。自从运输问题最早是由Hitchcock在1941年提出的[2][4]后，人们对这一类问题给予了极大的关注并进行了大量的研究，提出了有效解决问题的优化算法——单纯形法的变形[2]。但由于问题规模的扩大，纯数学方法在允许的时间和空间上都很难找到问题的最优解，于是人们开始转向寻找问题的近似最优解，遗传算法以其在求解最优化问题中的独特的自组织性、自适应性很快进入人们的视野。 经典的遗传算法对于求解多目标的NP完全性问题非常有效，但对于有多个限制条件的多目标最优化问题缺显得有点力不从心，很难得到稳定度较高、收敛较快的解。本文提出的双层遗传算法模型给这一类问题提供了一个很好的解决方法，虽然建模条件限制了描述的问题的通用性，有待推广，但它能有效利用问题的领域知识，提高了求解效率，保证了解的可行性和最优性，算法思想极具推广潜力，可在其它物流智能调度问题求解算法设计中借鉴。(The most optimal packet travel route problem is a kind of intelligent logistic scheduling problem is one of combinatorial calculation difficult problem many. Since the transportation problem was first proposed by Hitchcock in 1941 after the [2][4], the people of this class of problems given great attention and made a lot of research, the deformation of [2] optimization, simplex method algorithm is effective to solve the problem of. But due to the expansion of the scale of the problem, the pure mathematical methods in the allowed time and space are very difficult to find the optimal solution of the problem, so people begin to turn to the approximate optimal solution for the problem, genetic algorithm in solving optimization problems with its unique in the self-organizing, adaptive soon entered people s field of vision. Very effective classical genetic algorithm for solving multi-objective NP complete problems, but for the multi-objective optimization problem with multiple constraints. I)

三相并网逆变器下的LCL滤波器的谐波分析 给出逆变器输出电压和网侧电流的各次谐波表达式及其所占基波分量。 (lcl lcl lcl lcl)

说明：  认知无线电 能量检测matlab程序代码 认知无线电 能量检测matlab程序代码(energe detection energe detection energe detection energe detection energe detection energe detection energe detection energe detection )

it simulates dvb ofdm

距离判别法与Bayes判别法在分类中的应用。对于学习GUI同学很好。(Application of the distance discriminant law with the Bayes discriminant law classification the. Students for learning GUI good.)

graph compares pd versus signal noise ratio

CVaR的计算，以及cvar最优化的投资组合权重问题(CVaR OPTIMAL)

运用MCA算法，进行图像的分离，通过稀疏表示的K-SVD算法，将图像分为卡通和纹理部分(The MCA algorithm is used to separate the images. The sparse representation of the K-SVD algorithm is used to separate the images into cartoons and textures)

将压缩感知用于谱估计中，根据论文谱压缩感知的一些程序(Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e)