登录
首页 » matlab » Adaptive-CPSO-master

Adaptive-CPSO-master

于 2020-05-11 发布
0 167
下载积分: 1 下载次数: 1

代码说明:

说明:  改进的粒子群优化,有效解决全局最优,实现化工过程的温度优化(Improved particle swarm optimization to effectively solve global optimization)

文件列表:

Adaptive-CPSO-master, 0 , 2016-04-27
Adaptive-CPSO-master\.gitattributes, 378 , 2016-04-27
Adaptive-CPSO-master\.gitignore, 649 , 2016-04-27
Adaptive-CPSO-master\CEC2005, 0 , 2016-04-27
Adaptive-CPSO-master\CEC2005\A1.m, 6794 , 2016-04-27
Adaptive-CPSO-master\CEC2005\ACPSO.m, 6994 , 2016-04-27
Adaptive-CPSO-master\CEC2005\EF8F2_func_data.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\E_ScafferF6_M_D10.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\E_ScafferF6_M_D2.mat, 216 , 2016-04-27
Adaptive-CPSO-master\CEC2005\E_ScafferF6_M_D30.mat, 7384 , 2016-04-27
Adaptive-CPSO-master\CEC2005\E_ScafferF6_M_D50.mat, 20184 , 2016-04-27
Adaptive-CPSO-master\CEC2005\E_ScafferF6_func_data.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\README.txt, 5303 , 2016-04-27
Adaptive-CPSO-master\CEC2005\ackley_M_D10.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\ackley_M_D2.mat, 216 , 2016-04-27
Adaptive-CPSO-master\CEC2005\ackley_M_D30.mat, 7384 , 2016-04-27
Adaptive-CPSO-master\CEC2005\ackley_M_D50.mat, 20184 , 2016-04-27
Adaptive-CPSO-master\CEC2005\ackley_func_data.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\automataActSel.m, 347 , 2016-04-27
Adaptive-CPSO-master\CEC2005\automataProbUp.m, 636 , 2016-04-27
Adaptive-CPSO-master\CEC2005\b.m, 86 , 2016-04-27
Adaptive-CPSO-master\CEC2005\benchmark_func.m, 27327 , 2016-04-27
Adaptive-CPSO-master\CEC2005\body.m, 1681 , 2016-04-27
Adaptive-CPSO-master\CEC2005\elliptic_M_D10.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\elliptic_M_D2.mat, 216 , 2016-04-27
Adaptive-CPSO-master\CEC2005\elliptic_M_D30.mat, 7384 , 2016-04-27
Adaptive-CPSO-master\CEC2005\elliptic_M_D50.mat, 20184 , 2016-04-27
Adaptive-CPSO-master\CEC2005\exemplar.m, 1042 , 2016-04-27
Adaptive-CPSO-master\CEC2005\fbias_data.mat, 248 , 2016-04-27
Adaptive-CPSO-master\CEC2005\func_plot.m, 1716 , 2016-04-27
Adaptive-CPSO-master\CEC2005\global_optima.mat, 20184 , 2016-04-27
Adaptive-CPSO-master\CEC2005\griewank_M_D10.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\griewank_M_D2.mat, 216 , 2016-04-27
Adaptive-CPSO-master\CEC2005\griewank_M_D30.mat, 7384 , 2016-04-27
Adaptive-CPSO-master\CEC2005\griewank_M_D50.mat, 20184 , 2016-04-27
Adaptive-CPSO-master\CEC2005\griewank_func_data.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\high_cond_elliptic_rot_data.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func1_M_D10.mat, 8792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func1_M_D2.mat, 7592 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func1_M_D30.mat, 72792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func1_M_D50.mat, 200792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func1_data.mat, 8184 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func2_M_D10.mat, 8792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func2_M_D2.mat, 1112 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func2_M_D30.mat, 72792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func2_M_D50.mat, 200792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func2_data.mat, 8184 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func3_HM_D10.mat, 8792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func3_HM_D2.mat, 1112 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func3_HM_D30.mat, 72792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func3_HM_D50.mat, 200792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func3_M_D10.mat, 8792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func3_M_D2.mat, 1112 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func3_M_D30.mat, 72792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func3_M_D50.mat, 200792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func3_data.mat, 8184 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func4_M_D10.mat, 8792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func4_M_D2.mat, 1112 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func4_M_D30.mat, 72792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func4_M_D50.mat, 200792 , 2016-04-27
Adaptive-CPSO-master\CEC2005\hybrid_func4_data.mat, 8184 , 2016-04-27
Adaptive-CPSO-master\CEC2005\pso.m, 3030 , 2016-04-27
Adaptive-CPSO-master\CEC2005\rastrigin_M_D10.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\rastrigin_M_D2.mat, 216 , 2016-04-27
Adaptive-CPSO-master\CEC2005\rastrigin_M_D30.mat, 7384 , 2016-04-27
Adaptive-CPSO-master\CEC2005\rastrigin_M_D50.mat, 20184 , 2016-04-27
Adaptive-CPSO-master\CEC2005\rastrigin_func_data.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\rosenbrock_func_data.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\schwefel_102_data.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\schwefel_206_data.mat, 21040 , 2016-04-27
Adaptive-CPSO-master\CEC2005\schwefel_213_data.mat, 41104 , 2016-04-27
Adaptive-CPSO-master\CEC2005\sphere_func_data.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\table.asv, 75 , 2016-04-27
Adaptive-CPSO-master\CEC2005\table.m, 111 , 2016-04-27
Adaptive-CPSO-master\CEC2005\test.m, 5168 , 2016-04-27
Adaptive-CPSO-master\CEC2005\test_data.mat, 104928 , 2016-04-27
Adaptive-CPSO-master\CEC2005\weierstrass_M_D10.mat, 984 , 2016-04-27
Adaptive-CPSO-master\CEC2005\weierstrass_M_D2.mat, 216 , 2016-04-27
Adaptive-CPSO-master\CEC2005\weierstrass_M_D30.mat, 7384 , 2016-04-27
Adaptive-CPSO-master\CEC2005\weierstrass_M_D50.mat, 20184 , 2016-04-27
Adaptive-CPSO-master\CEC2005\weierstrass_data.mat, 984 , 2016-04-27
Adaptive-CPSO-master\Readme.md, 1996 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc, 0 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\ACPSO.m, 6432 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\automataActSel.m, 347 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\automataProbUp.m, 596 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\b.m, 86 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\body.m, 1654 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\body1.m, 466 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\body2.m, 469 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\clpso.m, 3411 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\exemplar.m, 1042 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\fit_func.m, 2226 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\icpsoh.m, 8487 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\learn.m, 6505 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\orthm_generator.m, 305 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\rcpsoh.m, 9065 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\splitswarm.m, 1256 , 2016-04-27
Adaptive-CPSO-master\TEC2006_RotatedFunc\test.m, 1046 , 2016-04-27
Adaptive-CPSO-master\TEC2006_StandardFunc, 0 , 2016-04-27

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • capon
    说明:  内含capon算法、求根music、谱估计music算法的简单举例。m文件格式,可直接运行,无错。(Capon includes algorithms, roots music, spectrum estimation of a simple algorithm, for example music. m file format, can be directly run, no mistakes.)
    2009-08-10 13:39:39下载
    积分:1
  • success
    网络能效,对于网络的吞吐量以及能效进行仿真,与网络相结合,验证其有效性(netThe network energy efficiency is simulated for the throughput and efficiency of the network, which is proved to be effective by combining with the network.)
    2018-11-05 16:50:12下载
    积分:1
  • a_guide_to_matlab
    说明:  一本MATLAB入门的书籍,适合入门,讲解的比较详细,值得一看(A MATLAB guide book which is for greenhand. )
    2010-04-19 09:40:57下载
    积分:1
  • PROGRAMS-USING-MATLAB
    CIRCULAR CONVOLUTION,CORRELATION,DIGITAL IIR FILTERS_BUTTERWORTH,DIGITAL IIR FILTERS_CHEBYSHEV, FIR FILTER DESIGN,FIR_TMS
    2012-04-03 22:22:35下载
    积分:1
  • chap14
    基于simulink的视频和图像处理,使用video and image processing blockset模块库进行视频和图像处理(Simulink based video and image processing, the use of video and image processing blockset module library for video and image processing)
    2014-12-25 17:51:47下载
    积分:1
  • kaiser
    kaiser滤波:用kaiserord生成阶数,及beta值(filter using kaiser)
    2010-06-21 22:01:45下载
    积分:1
  • GPSPP
    采用matlab实现了GPS单点定位,包括读N文件、O文件,定位解算,坐标转换等。(Matlab achieved using GPS single point positioning, including the reading of N documents, O file, locate solver, coordinate conversion.)
    2009-12-06 23:52:04下载
    积分:1
  • jiandanls-svm
    运用lssvm的分类用法,详细描述了每一部分的意义(Classification of LSSVM)
    2020-07-02 01:00:01下载
    积分:1
  • MUSIC
    MUSIC算法是一种基于矩阵特征空间分解的方法。从几何角度讲,信号处理的观测空间可以分解为信号子空间和噪声子空间,显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成,噪声子空间则由协方差矩阵中所有最小特征值(噪声方差)对应的特征向量组成。MUSIC算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱,所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率,同时适应于任意形状的天线阵列,但是原型MUSIC算法要求来波信号是不相干的。(MUSIC algorithm is a matrix decomposition method based on feature space. From the geometric point of view, the observed spatial signal processing can be decomposed into signal subspace and noise subspace, it is clear that the two spaces are orthogonal. The signal received by the array to the subspace of the covariance matrix of the signal component corresponding eigenvectors, the noise subspace of the covariance matrix of all by the smallest eigenvalue (noise variance) eigenvectors corresponding to the composition. MUSIC algorithm is the use of orthogonal properties between these two complementary space to estimate the spatial orientation of the signal. Noise subspace of all vectors are used to construct the spectrum, all the spatial orientation of the spectrum corresponding to the peak position of the signal wave direction. MUSIC algorithm greatly improves the resolution measurements, while the antenna array adapted to any shape, but the prototype MUSIC algorithm requires to wave sign)
    2014-02-19 13:23:48下载
    积分:1
  • chap3
    《visual c++matlab图像处理与识别实用案例精选》源码(" Visual c++ matlab image processing and recognition of practical cases selected" source)
    2009-05-09 22:48:44下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载