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Optimize
MATLAB 6_5 辅助优化计算与设计
1、文件命名说明
a)文件夹“第1章”中的文件对应书中第1章的例程，其他以此类推；
b) 文件名exampleX1_X2.m：对应例程X1_X2
例如：example2_1表示例程2_1.
2、注意
为了方便起见，书中的每一个例程存为一个文件；而有些例程中将其调用的函数文件也放在这个例程文件中，所以读者在运行光盘中的例程文件时注意这一点，如果是这样的例程文件应该将其调用的函数文件分离出来另存为一个文件。(MATLAB 6_5 Auxiliary and Design Optimization 1, Note document naming a) folder "Chapter 1" in the document corresponding book Chapter 1 of the routines, other analogy; b) Name exampleX1_X2.m : X1_X2 corresponding routines such as : example2_1 said two compression routines. 2, the attention for the sake of convenience, Each book deposit for a routine one; Some routines will call a function of its documents on the routines document, Therefore, the reader is running the routines CD document to this point, If this is the routine document should be called to separate the function documents saved as a file.)
- 2006-11-23 17:07:55下载
- 积分：1
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Hu
说明： 图像特征提取，计算图像的7个Hu不变矩特征，matlab例程(Seven Hu invariant moments features of images)
- 2019-05-15 19:43:18下载
- 积分：1
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ants_arithgom
aoc.m 是用matlab实现蚁群算法ants_information.m蚁群算法中信息素的构建
mcl是马尔科夫聚类算法
nmf是矩阵的非负分解
Generative Topographic Mapping工具包(aoc.m is matlab implementation of ant colony algorithm ants_information.m pheromone ant colony algorithm is a Markov clustering algorithm to build mcl is nmf non-negative matrix decomposition of Generative Topographic Mapping Toolkit)
- 2010-03-13 14:44:41下载
- 积分：1
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wavelet
说明： 基于小波系数块能量分析的自适应数字水印算法(Energy analysis based on wavelet coefficients of adaptive digital watermarking algorithm)
- 2010-05-03 10:30:13下载
- 积分：1
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jisuangeidingxinyuandeshang
离散无记忆信源是一种最简单且最重要的信源，可以用完备的离散型概率空间来描述。本实验通过计算给定的信源的熵，加深对信源及其扩展信源的熵的概念的理解。(Discrete memoryless sources is one of the most simple and most important information source, can be used to describe a complete discrete probability space. In this study, by computing the entropy of a given source, to deepen understanding of the source and the expansion of the concept of entropy of the source.
)
- 2012-04-07 12:48:10下载
- 积分：1
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os-elm
实现增量极限学习机，在线学习的方法等等，实现了在线连续学习功能(code written in matlab)
- 2012-04-19 18:26:38下载
- 积分：1
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Digital-baseband-system
数字基带系统将数字信号从时域上看时数字滚降，需要画出滚降前后的波形展示出来。频域上带宽变化了。输入滤波器之前的波形与采样判决之后的波形。采样判决前的频谱。
采样判决后的码型。
2、眼图（采样判决之前的眼图）（信噪比大眼图清晰）
3、误码曲线，（横坐标时信噪比，纵坐标时误码率）
4、Arfa变化对误码特性的影响，变好还是变差(变好)，眼图清晰度变化（清晰），传递函数的带宽的变化。（带宽越大）(1.Digital baseband system point of view the digital signal from the time domain when the number of roll-off, you need to draw the waveform displayed before and after the roll-out. The frequency domain, the bandwidth change. Input filter and sample ruling before the wave after wave. Sample pre-judgment spectrum.
Sampling pattern after the verdict.
2, the eye diagram (eye before sampling decision) (SNR big eye clear)
3, the error curve (abscissa when the signal to noise ratio, bit error rate when the vertical axis)
4, Arfa impact of change on BER performance, get better or worse (getting better), change of eye-definition (clarity), the transfer function of the bandwidth changes. (The greater the bandwidth))
- 2011-06-03 23:04:22下载
- 积分：1
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the-Null-Preprocessing-Algorithm
阵列信号处理中的零点预处理算法，内含基本算法及对旁瓣的改进算法，注释很详细。另附本人做的利用该算法进行阵列信号处理的仿真PPT。(the zero_point preprocessing algorithm in array signal processing, containing the basic algorithm and improved algorithm for sidelobe, very detailed notes. Attached I do use this algorithm for array signal processing simulation PPT.)
- 2021-01-03 11:38:57下载
- 积分：1
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winrats
说明： 如何用winrats做garch-bekk模型(how to use winrats creat garch-bekk)
- 2019-04-30 15:40:05下载
- 积分：1
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bbbb
摘 要:提出一种新的基于Pareto多目标进化免疫算法(PMEIA)。算法在每一代进化群体中选取最优非支配抗体保存到记忆细胞文档中 同时引入Parzen窗估计法计算记忆细胞的熵值,根据熵值对记忆细胞文档进行动更新,使算法向着理想Pareto最优边界搜索。此外,算法基于点在目标空间分况进行克隆选择,有利于得到分布较广的Pareto最优边界,且加快了收敛速度。与已有算法相比, PMEIA在收敛性、多样性,以及解的分布性方面都得到很好的提高。(Abstract:This paperproposed a new pareto-based multi-object evolutionary mi mune algorithm(PMEIA). PMEIA selected
optmi alnon-dominated antibodieswhichwere then reserved inmemory cellarchive, and introducedParzenwindow to calculate
entropy ofmemory cells. Updated thememory cell archive according to entropy ofmemory cells. This guarantees the conver-
gence to the true Pareto fron.t Moreover, the performance of clone selection was dependent on distribution in the objective
space, whichwas favorable forgetting awidely spreadPareto frontand mi proving convergence speed. Comparedwith the exis-
ted algorithms, the obtained solutions ofPMEIA havemuch betterperformance in the convergence, diversity and distribution.)
- 2010-01-09 13:08:12下载
- 积分：1
