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计算机工程 ›› 2009, Vol. 35 ›› Issue (23): 190-193.doi: 10.3969/j.issn.1000-3428.2009.23.066

• 人工智能及识别技术 • 上一篇    下一篇

基于混沌离散粒子群优化的约束性多分类模型

计智伟1,2，吴耿锋2，胡 珉3   

  1. (1. 浙江林学院信息工程学院，临安311300；2. 上海大学计算机工程与科学学院，上海 200072；3. 上海大学悉尼工商学院，上海 200072)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-12-05 发布日期:2009-12-05

Restrictive Multi-classes Classification Model Based on Chaotic Binary Particle Swarm Optimization

JI Zhi-wei1,2, WU Geng-feng2, HU Min3   

  1. (1. School of Information Engineering, Zhejiang Forestry University, Linan 311300; 2. School of Computer Engineering & Science, Shanghai University, Shanghai 200072; 3. Sydney Institute of Language & Commerce, Shanghai University, Shanghai 200072)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-12-05 Published:2009-12-05

PDF

768

被引次数

3 | 2

摘要： 约束性多分类问题是在某些工程和生产领域中存在的一类具有特殊约束条件的多分类模式识别问题。针对传统的有监督分类法无法解决约束性多分类问题，提出一种基于混沌离散粒子群优化的约束性多分类模型(CBPSO-RMCM)，并将该模型应用于盾构隧道管片选型预测。仿真实验表明，CBPSO-RMCM模型能有效地实现约束性多分类模式识别，并且分类准确率较高。

关键词: 约束性, 多分类, 混沌, 粒子群优化

Abstract: The problem of restrictive multi-classes classification is a kind of multi-classes pattern recognition subject which contains specific restrictive condition in some engineering and production area. Aiming at the question that the traditional supervised classification method could not solve the problem of restrictive multi-classes classification, this paper proposes a Restrictive Multi-classes Classification Model based on Chaotic Binary Particle Swarm Optimization(CBPSO-RMCM), and uses this model to predict the selection of segment type in shield tunneling process. Simulation experiment shows that the CBPSO-RMCM model is capable of solving the problem of restrictive multi-classes classification, and has high classification accuracy.

Key words: restrictive, multi-classes classification, chaos, Particle Swarm Optimization(PSO)

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