Optimization design of fuzzy classifcation system based on QPSO

ZHU Yun-hui; SUN Jun

doi:10.13873/J.1000-9787(2017)10-0089-03

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Optimization design of fuzzy classifcation system based on QPSO

更新时间：2025-09-11

- Optimization design of fuzzy classifcation system based on QPSO
- Vol. 36, Issue 10, Pages: 89-91(2017)
- 作者机构：
  
  江南大学物联网工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2017)10-0089-03
  CLC： TP18
- Published：2017
- 稿件说明：
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[1]诸云晖,孙俊.基于QPSO的模糊分类系统优化设计[J].传感器与微系统,2017,36(10):89-91.

ZHU Yun-hui, SUN Jun. Optimization design of fuzzy classifcation system based on QPSO[J]. 2017, 36(10): 89-91.
[1]诸云晖,孙俊.基于QPSO的模糊分类系统优化设计[J].传感器与微系统,2017,36(10):89-91. DOI： 10.13873/J.1000-9787(2017)10-0089-03.

ZHU Yun-hui, SUN Jun. Optimization design of fuzzy classifcation system based on QPSO[J]. 2017, 36(10): 89-91. DOI： 10.13873/J.1000-9787(2017)10-0089-03.

摘要

提出了构建模糊分类系统的有效方法。通过量子位选择的方法对初始的模糊规则进行优化

减少种群规模、提高全局搜索能力

且可以大幅缩短训练时间

达到快速收敛、有效分类的目的。为了优化模糊分类空间和减少模糊规则数目

提出了量子行为粒子群优化（QPSO）算法

提高初始模糊分类系统的性能。实验结果证明:优化方法较之其他方法更有效率

准确率更高。

Abstract

An effective method for construction of fuzzy classification system（ FCS） is proposed. In FCS

the initial fuzzy rules are optimized with a quantum bit which has many unique advantages such as small population size

fast convergence

short training time and strong global search ability. After then

in order to accomplish the optimization for the fuzzy classification space and reduce number of fuzzy rules

quantum-behaved particle swarm optimization（QPSO）algorithm is proposed to improve characteristics of initial FCS. The experimental result demonstrates that this method is more efficient and accurate than other methods without QPSO.

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references

采用ZigBee和模糊PID控制的水声信号采集系统 [J]. 张国光. 舰船电子工程 . 2015(09)

基于模糊模拟的风险投资项目决策 [J]. 赵振武,唐万生. 模糊系统与数学 . 2007(04)

快速运动过程中双足机器人重心稳定的自适应模糊控制 [J]. 谢黎明,查富生,李国慧. 机床与液压 . 2004(09)

Quantum Computing: An Introduction [J] . J.Dale Prince. Journal of Electronic Resources in Medical Libraries . 2014 (3)

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