期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

微粒群多元最优信息的模糊自适应规划算法 被引量：1

Fuzzy Adaptive Programming Algorithm Based on Particle Swarm Multi-optimum Information
下载PDF
导出
摘要 在静态的微粒群多元最优信息规划模式的基础上,提出了微粒群多元最优信息的模糊自适应规划算法.该算法将模糊逻辑引入微粒群多元最优分布状态的动态规划,以实现多元最优信息间规划比例在寻优过程中的自适应调节,从而能够更大程度地改善其总体寻优性能.以最优和次最优分布信息的规划为例,构造了一种基于单变量两维状态输入模糊控制结构的微粒群模糊自适应规划引导器,并加以仿真计算. Fuzzy adaptive programming algorithm based on particle swarm multi-optimum is proposed on the basis of the static particle swarm multi-optimum information programming mode. Fuzzy logic is introduced into the process of multi-optimum distribution state dynanuic programming, so that the proportion factor of multi-optimum programming can be dynamically adjusted in the optimization process, and therefore its performance can be improved greatly. A kind of particle swarm adaptive programming algorithm based on single-variable and two-dimension-input fuzzy control structure of optimum and sub-optimum distribution information is put forward and simulated.
作者 康琦 汪镭 吴启迪
机构地区 同济大学电子与信息工程学院
出处 《信息与控制》 CSCD 北大核心 2005年第4期439-443,450,共6页 Information and Control
基金 国家自然科学基金资助项目(70271035 60104004) 上海市启明星计划资助项目(03QG14053) 国家973计划资助项目(2002CB312202)
关键词 模糊逻辑 多元最优信息动态规划 微粒群算法 fuzzy logic multi-optimum information dynamic programming particle swarm optimization
分类号 TP273.2 [自动化与计算机技术—检测技术与自动化装置]
  • 相关文献

参考文献10

  • 1Kennedy J, Eberhart R C. Particle swarm optimization [A]. Proceedings of the 1995 IEEE International Conference on Neural Networks [C]. New York, NY, USA: IEEE, 1995. 1942-1948.
  • 2Eberhart R C, Shi Y. Particle swarm optimization : developments,applications and resources [ A]. Proceedings of the 2001 Congress on Evolutionary Computation [ C ]. Piscataway, NJ. USA:IEEE. 2001. 81 -86.
  • 3Eberhart R C, Shi Y, Comparing inertia weights and constriction factors in particle swarm optimization [ A ]. Proceedings of the 2000 Congress on Evolutionary Computation [ C ]. Piseataway,NJ, USA: IEEE, 2000. 84 - 88.
  • 4Fukuyama Y, Yoshida H. A particle swarm optimization for reactive power and voltage control in electric power systems [ A ].Proceedings of the 2001 Congress on Evolutionary Computation[C]. Piscataway, NJ, USA: IEEE, 2001. 87-93.
  • 5Sousa T, Silva A. Particle swarm based data mining algorithms for classification tasks [J]. Parallel Computing, 2004, 30(5 -6) :767 - 783.
  • 6Clere M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization [ A ]. Proceedings of the 1999 Congress on Evolutionary. Computation [ C ]. Piscataway,NJ, USA: IEEE, 1999. 1951-1957.
  • 7Fan H Y. A modification to particle swarm optimization algorithm [J]. Engineering Computations, 2002, 19(8.) : 970 -989.
  • 8Shi Y, Eberhart R C. Empirical study of particle swarm optimization [ A]. Proceedings of the 1999 Congress on Evolutionary Computation [C]. Piseataway, NJ, USA: IEEE, 1999. 1945 -1950.
  • 9Shi Y, Eberhart R. Fuzzy adaptive particle swarm optimization[ A]. Proceedings of the 2001 Congress on Evolutionary Computation [C]. Piscataway, NJ, USA: IEEE, 2001. 101 - 106.
  • 10Wang L, Kang Q, Wu Q D. Multi-optimum programming based particle swarm optimization algorithm and its application in multidimensional & multi-modal function optimization [ A]. Proceedings of the 2004 IEEE International Conference on Control Application [C]. Piscataway, NJ, USA: IEEE, 2004. 149 -152.

同被引文献7

引证文献1

二级引证文献6

信息与控制
2005年 第4期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部