文件名称:PSOsuanfa
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- 上传时间：
- 2018-03-08
- 文件大小：
- 735kb
- 下载次数：
- 0次
- 提 供 者：
- liyang******
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
该代码为基于变异粒子群算法的函数极值寻优算法，选择合适的参数，通过迭代寻优，最后得到最优个体的适应度(This code is a function extremum optimization algorithm based on the mutation particle swarm optimization algorithm, selects the appropriate parameters, and obtains the optimal individual fitness by iterative optimization.)
相关搜索： 神经网络
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下载文件列表
| 文件名 | 大小 | 更新时间 |
|---|---|---|
| PSOsuanfa | 0 | 2018-03-08 |
| PSOsuanfa\chapter1 | 0 | 2018-03-08 |
| PSOsuanfa\chapter1\BPDLX.m | 6159 | 2013-08-21 |
| PSOsuanfa\chapter1\chapter1_1.m | 4030 | 2013-08-21 |
| PSOsuanfa\chapter1\data1.mat | 93015 | 2009-08-29 |
| PSOsuanfa\chapter1\data2.mat | 92845 | 2009-08-29 |
| PSOsuanfa\chapter1\data3.mat | 92937 | 2009-08-29 |
| PSOsuanfa\chapter1\data4.mat | 93438 | 2009-08-29 |
| PSOsuanfa\chapter2 | 0 | 2018-03-08 |
| PSOsuanfa\chapter2\BP_Hidden.m | 3411 | 2013-08-21 |
| PSOsuanfa\chapter2\chapter2_1.m | 3404 | 2014-03-06 |
| PSOsuanfa\chapter2\data.mat | 46375 | 2009-12-14 |
| PSOsuanfa\chapter3 | 0 | 2018-03-08 |
| PSOsuanfa\chapter3\Code.m | 420 | 2009-08-16 |
| PSOsuanfa\chapter3\Cross.m | 1556 | 2009-08-31 |
| PSOsuanfa\chapter3\Decode.m | 1158 | 2009-08-31 |
| PSOsuanfa\chapter3\Genetic.m | 4299 | 2010-11-28 |
| PSOsuanfa\chapter3\Mutation.m | 1602 | 2009-11-12 |
| PSOsuanfa\chapter3\PSO.m | 5046 | 2010-11-28 |
| PSOsuanfa\chapter3\Select.m | 853 | 2013-08-21 |
| PSOsuanfa\chapter3\data.m | 134 | 2009-09-18 |
| PSOsuanfa\chapter3\data.mat | 46404 | 2009-09-18 |
| PSOsuanfa\chapter3\fun.m | 1013 | 2010-11-27 |
| PSOsuanfa\chapter3\test.m | 291 | 2009-08-31 |
| PSOsuanfa\chapter4 | 0 | 2018-03-08 |
| PSOsuanfa\chapter4\BP.m | 3426 | 2016-11-28 |
| PSOsuanfa\chapter4\Code.m | 420 | 2009-08-16 |
| PSOsuanfa\chapter4\Cross.m | 1556 | 2009-08-16 |
| PSOsuanfa\chapter4\Genetic.m | 2654 | 2010-11-28 |
| PSOsuanfa\chapter4\Mutation.m | 1545 | 2009-08-16 |
| PSOsuanfa\chapter4\Select.m | 823 | 2014-09-25 |
| PSOsuanfa\chapter4\data.m | 135 | 2010-07-25 |
| PSOsuanfa\chapter4\data.mat | 93688 | 2016-11-28 |
| PSOsuanfa\chapter4\data1.mat | 92580 | 2016-11-28 |
| PSOsuanfa\chapter4\fun.m | 326 | 2009-09-15 |
| PSOsuanfa\chapter4\net.mat | 535 | 2009-09-11 |
| PSOsuanfa\chapter4\test.m | 278 | 2009-12-26 |
| PSOsuanfa\chapter5 | 0 | 2018-03-08 |
| PSOsuanfa\chapter5\Bp_Ada_Fore.m | 3851 | 2010-11-28 |
| PSOsuanfa\chapter5\Bp_Ada_Sort.m | 4297 | 2010-11-28 |
| PSOsuanfa\chapter5\data.mat | 11820 | 2009-12-28 |
| PSOsuanfa\chapter5\data1.mat | 46394 | 2009-09-09 |
| PSOsuanfa\chapter6 | 0 | 2018-03-08 |
| PSOsuanfa\chapter6\MPID.m | 5592 | 2010-11-28 |
| PSOsuanfa\chapter6\MPIDCS.m | 5505 | 2010-11-28 |
| PSOsuanfa\chapter6\MPIDDLX.m | 6056 | 2010-11-28 |
| PSOsuanfa\chapter6\draw.m | 5697 | 2010-01-23 |
| PSOsuanfa\chapter6\fun.m | 5092 | 2009-11-15 |
| PSOsuanfa\chapter6\pso.m | 9133 | 2010-11-28 |