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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

  • Apsmpdemo1
    说明：  一个casio dt 900 源代码，仅供参考！(A casio dt 900 source code for reference purposes only!)
    2011-03-02 11:33:57下载
    积分：1
  • paper_about_matlab
    一次关于用matlab实现的信号处理的文章期刊，希望有所帮助。(Meeting on implementation of signal processing with matlab journal articles, I hope help.)
    2011-05-03 20:49:29下载
    积分：1
  • Nouveau-dossier-(2)
    U MSUT READ THIS FILE COS IT VERY INTERSTING FOR YOU
    2013-09-27 00:33:52下载
    积分：1
  • PML_10
    交错网格声波方程正演模拟，PML吸收边界，比较适合刚刚接触的学者。交错网格有限差分地震波场计算，二维的声波正演，利用空间10阶，时间2阶。(The CalCulation of the SeismiC Wave一 fieldwith Staggered-grid Finite difference Sheme)
    2014-01-09 08:54:14下载
    积分：1
  • b
    说明：  ：DBSCAN是一个基于密度的聚类算法。该算法将具有足够高密度的区域划分为簇，并可以在带有“噪声”的空间数 据库中发现任意形状的聚类。但DtLqCAN算法没有考虑非空间属性，且DBSCAN算法需扫描空间数据库中每个点的e一 邻域来寻找聚类，这使得DBSCAN算法的应用受到了一定的局限。文中提出了一种基于DBSCAN的算法，可以处理非空 间属性，同时又可以加快聚类的速度。(: DBSCAN is a density-based clustering algorithm. The algorithm has a sufficiently high density area is divided into clusters, and to be with the &quot noise&quot found in the spatial database clusters of arbitrary shape. But DtLqCAN algorithm did not consider non-spatial attributes, and spatial database DBSCAN algorithm to be scanned for each point e in the neighborhood to find a cluster, DBSCAN algorithm which makes the application subject to certain limitations. In this paper, an algorithm based on DBSCAN can handle the non-spatial attributes, can also speed up the clustering speed.)
    2010-12-06 16:38:45下载
    积分：1
  • khatisazi
    linear the sychronous machin
    2012-06-15 00:50:36下载
    积分：1
  • ziyoushouxieti
    文档内容是基于MATLAB的手写体字符识别系统的设计(The content of the document is to design MATLAB handwritten character recognition system based on)
    2015-04-01 17:22:13下载
    积分：1
  • PSO-and-PSOEM-simulation
    基于扩展记忆的粒子群优化算法（Particle Swarm Optimization based on memory）(This paper combines SVM with improved PSO (Particle Swarm Optimization with Extended Memory, PSOEM) and then builds PSOEM-SVM forecasting model. The PSOEM searches the solution space intelligently and finds out the best one. Parameters in SVM are optimized by PSOEM, which implements automation of the parameter optimization avoiding the blindness of selecting parameters. Not only does it utilize the generalization feature of SVM, but enhance the global search ability of PSO (Particle Swarm Optimization). Thus, both accuracy and speed are increased at the same time.)
    2012-04-29 20:47:48下载
    积分：1
  • DOA_MUSIC_SuanFa
    DOA MUSIC 算法。调试好的，保证能运行。王永良 空间谱估计理论与算法 第110页的仿真程序。(DOA MUSIC algorithm )
    2010-07-01 16:32:14下载
    积分：1
  • ofdd
    OFDM process with FFT submission
    2010-09-21 07:05:42下载
    积分：1
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