关于混沌Kolmogorov熵的计算程序 K entropy

ex5_1 I-型线性相位FIR滤波器 ex5_2 II-型线性相位FIR滤波器 ex5_3 III-型线性相位FIR滤波器 ex5_4 IV-型线性相位FIR滤波器 ex5_5 矩形窗频响 ex5_6 希尔伯特变换器设计－汉宁窗 ex5_7 低通滤波器设计－汉明窗 ex5_8 带通滤波器设计－布莱克曼窗 ex5_9 低通滤波器设计－凯塞窗 ex5_11 频率采样技术：低通，朴素法 ex5_12 频率采样技术：低通, 最优法T1 & T2 ex5_13 频率采样技术：带通, 最优法T1 & T2 ex5_14 频率采样技术：高通, 最优法T1 ex5_15 频率采样技术：差分器 ex5_16 频率采样技术： 希尔伯特变换器 ex5_17 利用Parks-McClella算法设计低通滤波器 ex5_18 用PM算法进行的带通滤波器设计 ex5_19 用PM算法进行的高通滤波器设计 ex5_20 用PM算法进行的阶梯滤波器设计 ex5_21 用PM算法进行的差分器设计 ex5_22 用PM算法进行的希尔伯特变换器设计(ex5_1 I-type linear phase FIR filter ex5_2 II-type linear phase FIR filter ex5_3 III-type linear phase FIR filter ex5_4 IV-type linear phase FIR filter rectangular window frequency response ex5_5 Hilbert ex5_6 converter design- Hanning window low-pass filter design ex5_7- Hamming window ex5_8 bandpass filter design- Blackman window ex5_9 low-pass filter design- Kaiser window ex5_11 sampling frequency: low-pass, a simple method ex5_12 sampling frequency: low-pass, optimal method T1 & T2 sampling frequency ex5_13: band-pass, optimal method T1 & T2 ex5_14 sampling frequency: high-pass, optimal method T1 sampling frequency ex5_15: differentiator sampling frequency ex5_16: Hilbert converter Parks-McClella use ex5_17 algorithm design low-pass filter PM algorithm ex5_18 with the band-pass filter design PM algorithm ex5_19 with the high-pass filter design PM algorithm used ex5_20 ladder filter design PM algorithm used ex5_21 differential design PM al)

The simulink of ofdm system.

说明：  MATLAB有限元分析与应用的源代码，非常实用！(MATLAB finite element analysis and application source code, very useful!)

HEED algorithm on Matlab

在Kraken简正模模型中，用于更改*.FLD场文件的matlab程序(Kraken normal mode model for change* FLD field file matlab program)

语音信号分析处理实验，主要是线性预测分析的matlab代码(Speech signal analysis and processing experiments, mainly linear predictive analysis matlab code)

polar code在AWGN信道下采用连续删除译码程序(polar code in AWGN channel with successive cancellation decoding 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)

MCSE Training Guide: Networking Essentials, Second Edition is designed for advanced end-users, service technicians, and network administrators with the goal of certification as a Microsoft Certified Systems Engineer (MCSE). The “Networking Essentials” exam (70-058) measures your ability to implement, administer, and troubleshoot information systems that incorporate Windows 95 as well as any of the BackOffice family of products. According to Microsoft, the exam covers only the networking knowledge and skills common to both Windows 95 and BackOffice products.

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