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于 2021-04-13 发布
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说明：  matlab智能算法数值分析数值优化路径规划人工智能神经网络(Matlab intelligent algorithm 30 examples, learn numerical algorithms and artificial intelligence path planning numerical optimization)

文件列表：

chapter1, 0 , 2021-04-13
chapter1\example1.m, 1909 , 2010-10-31
chapter1\example2.m, 2113 , 2010-10-31
chapter1\Sheffield的遗传算法工具箱.rar, 423860 , 2015-06-14
chapter10, 0 , 2021-04-13
chapter10\data.mat, 422 , 2010-12-28
chapter10\main.m, 6048 , 2010-12-28
chapter11, 0 , 2021-04-13
chapter11\aberranceJm.m, 1067 , 2007-09-24
chapter11\across.m, 2329 , 2007-09-17
chapter11\cal.m, 1325 , 2007-09-17
chapter11\calp.m, 555 , 2007-09-17
chapter11\caltime.m, 1276 , 2007-09-17
chapter11\Find.m, 178 , 2007-08-22
chapter11\main.m, 2816 , 2015-06-18
chapter11\plotRec.m, 487 , 2007-07-14
chapter11\ranking.M, 4708 , 2010-12-23
chapter11\REINS.M, 5574 , 1998-04-22
chapter11\RWS.M, 1090 , 1998-04-22
chapter11\scheduleData.mat, 527 , 2010-12-23
chapter11\SELECT.M, 2401 , 1998-04-22
chapter11\selectJm.m, 398 , 2007-09-24
chapter12, 0 , 2021-04-13
chapter12\bestselect.m, 1669 , 2010-09-06
chapter12\centre.fig, 7910 , 2010-09-07
chapter12\concentration.m, 479 , 2010-09-06
chapter12\Cross.m, 1294 , 2010-09-06
chapter12\draw.m, 1046 , 2010-09-06
chapter12\excellence.m, 400 , 2010-09-06
chapter12\figure.fig, 9007 , 2010-09-07
chapter12\fitness.m, 901 , 2010-09-07
chapter12\IAdata.mat, 4838 , 2010-09-07
chapter12\incorporate.m, 1102 , 2010-09-06
chapter12\main.m, 3676 , 2010-12-28
chapter12\Mutation.m, 1001 , 2010-09-06
chapter12\popinit.m, 319 , 2010-09-06
chapter12\Select.m, 912 , 2010-09-06
chapter12\similar.m, 377 , 2010-09-06
chapter12\test.m, 580 , 2010-09-06
chapter13, 0 , 2021-04-13
chapter13\sample1, 0 , 2021-04-13
chapter13\sample1\fun.m, 241 , 2010-08-03
chapter13\sample1\main.m, 1579 , 2010-08-05
chapter13\sample1\MexicoHatnew.m, 174 , 2010-08-03
chapter13\sample1\PSO0.m, 1802 , 2010-08-05
chapter13\sample1\PSO1.m, 1859 , 2010-08-05
chapter13\sample1\PSO2.m, 1859 , 2010-08-05
chapter13\sample1\PSO3.m, 1878 , 2010-08-05
chapter13\sample1\PSO4.m, 1863 , 2010-08-05
chapter13\sample1\wchange.m, 353 , 2010-08-05
chapter13\sample2-Rastrgrin, 0 , 2021-04-13
chapter13\sample2-Rastrgrin\fun.m, 197 , 2010-08-09
chapter13\sample2-Rastrgrin\pso.fig, 6256 , 2010-08-09
chapter13\sample2-Rastrgrin\PSO.m, 1571 , 2010-08-09
chapter13\sample2-Rastrgrin\pso.mat, 2624 , 2010-08-09
chapter13\sample2-Rastrgrin\rastrigrin.fig, 123876 , 2010-08-09
chapter13\sample2-Rastrgrin\rastrigrin.m, 128 , 2010-08-09
chapter13\sample3-Griewankan, 0 , 2021-04-13
chapter13\sample3-Griewankan\fun.m, 209 , 2010-08-09
chapter13\sample3-Griewankan\Griewank.fig, 699309 , 2010-08-09
chapter13\sample3-Griewankan\Griewank.m, 146 , 2010-08-09
chapter13\sample3-Griewankan\pso.fig, 5867 , 2010-08-11
chapter13\sample3-Griewankan\PSO.m, 1582 , 2010-08-11
chapter13\sample3-Griewankan\pso.mat, 4509 , 2010-08-11
chapter14, 0 , 2021-04-13
chapter14\GA_run.m, 477 , 2010-08-23
chapter14\PID_Model.mdl, 29558 , 2010-08-22
chapter14\PSO.m, 2589 , 2010-08-23
chapter14\PSO_PID.m, 174 , 2010-08-22
chapter14\问题解决思路.pdf, 116504 , 2010-08-23
chapter15, 0 , 2021-04-13
chapter15\bayg29.txt, 695 , 2009-06-12
chapter15\burma14.txt, 236 , 2009-06-12
chapter15\ch130.txt, 4394 , 2009-06-12
chapter15\ch150.txt, 5098 , 2009-06-12
chapter15\dist.m, 126 , 2009-06-12
chapter15\eil51.txt, 444 , 2009-06-12
chapter15\fitness.m, 419 , 2010-10-16
chapter15\gr96.txt, 1628 , 2009-06-12
chapter15\main.m, 5566 , 2011-03-19
chapter15\Oliver30.txt, 434 , 2009-06-12
chapter15\pr226.txt, 3459 , 2009-06-12
chapter15\pr76.txt, 1117 , 2009-06-12
chapter15\st70.txt, 678 , 2009-06-12
chapter16, 0 , 2021-04-13
chapter16\DF1function.m, 586 , 2010-11-16
chapter16\fitnessRecord.mat, 4236 , 2010-11-12
chapter16\main.m, 2689 , 2015-06-30
chapter16\result.mat, 12567 , 2010-06-29
chapter17, 0 , 2021-04-13
chapter17\PSOt, 0 , 2021-04-13
chapter17\PSOt\forcecol.m, 172 , 2004-04-27
chapter17\PSOt\forcerow.m, 181 , 2004-04-27
chapter17\PSOt\goplotpso.m, 5790 , 2010-11-04
chapter17\PSOt\linear_dyn.m, 749 , 2004-08-23
chapter17\PSOt\normmat.m, 4588 , 2006-03-17
chapter17\PSOt\pso_Trelea_vectorized.m, 22526 , 2009-12-20
chapter17\PSOt\spiral_dyn.m, 841 , 2004-08-27
chapter17\testfunctions, 0 , 2021-04-13
chapter17\testfunctions\ackley.m, 871 , 2004-08-23

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0 个回复

  • MATLAB-GUI
    matlab GUI编辑的很好讲解，对初学者很有帮助的文档(matlab GUI，helpful to the newer)
    2012-03-22 15:39:39下载
    积分：1
  • makebottle
    function of autoassociative neural network
    2014-12-01 06:24:20下载
    积分：1
  • RCB
    对对角加载算法进行了分析,基于 RCB算法提出了一种可以抑制运动干扰的稳健波束形成器。可根据干扰位置的统计模型进行零陷扩宽,从而有效的抑制运动干扰。(An implementation algorithm of Robust Capon Beamforming is presented. This algorithm is advanced and widely used in the field of signal processing and communication.)
    2020-09-09 10:38:03下载
    积分：1
  • First_EEE
    语音信号线性特征的能量熵特征的计算，这是第一能量熵的源码，希望大家可以借鉴一下(Linear characteristics of speech signal characteristics of the energy calculation of entropy, this is the first energy entropy of the source, I hope we can learn about)
    2010-09-10 18:43:22下载
    积分：1
  • circle
    周期图法功率谱估计，希望对大家能有所帮助。(Periodogram Power Spectral Estimation)
    2010-02-07 20:22:25下载
    积分：1
  • scheinerman
    Companion Software
    2010-02-17 07:14:27下载
    积分：1
  • demod
    bpsk 的源代码 MATLAB 很好的 希望有帮助(802.11b 1Mbps PHY link)
    2011-05-03 22:14:32下载
    积分：1
  • ex92.m
    speech analysis windowing of speech signal
    2012-04-12 14:39:05下载
    积分：1
  • Laplace
    传统的短时谱估计语音增强算法通常假设语音谱分量相互独立，没有考虑语音谱分量间的相关性。针对这 一问题，该文提出一种新的基于多元Laplace分布模型的短时谱估计算法。首先，假设语音的离散余弦变换(DCT) 系数服从多元Laplace分布，以此利用谱分量间的相关性；在此基础上，利用多元随机矢量的高斯尺度混合模型表 示，推导得到语音DCT系数矢量的最小均方误差(MMSE)估计的解析表达式；并进一步推导了基于该分布模型的 语音存在概率，对最小均方误差估计子进行修正。实验结果表明，该算法在抑制背景噪声和减少语音失真等方面优 于传统的语音增强方法。(The spectral components of speech are usually assumed to be independent in traditional short-time spectrum estimation, which is not the case in practice. Tosolve this problem, a new speech enhancement algorithm with multivariate Laplace speech model is proposed in this paper. Firstly, the speech Discrete Cosine Transform (DCT) coefficients are modeled by a multivariate Laplace distribution, so the correlations between speech spectral components can be exploited. And then a Minimum-Mean-Square-Error (MMSE) estimator based on the proposed model is derived using a Gaussian scale mixture representation of random vectors. Furthermore, the speech presence uncertainty with the new model is derived to modify the MMSE estimator. Experimental results show that the developed method has better noise suppression performance and lower speech distortion compared to the traditional speech enhancement method. )
    2014-01-18 10:44:40下载
    积分：1
  • qingzhuoyin
    本设计采用了一种基于三参数组合的方法对语音信号进行了清/浊音判决。在传统两参数（短时能量和过零率）算法基础上增加了自相关函数作为判决参数，减少了清/浊音信号误判率。(This design uses a combination of methods based on three parameters of the voice signals of the Qing/voiced sentence. In the traditional two parameters (short-term energy and zero crossing rate) algorithm based on the increase since the correlation function as a decision parameter, reducing the clearance/voiced signal false positives.)
    2011-10-18 18:51:36下载
    积分：1
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