计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2009年
23期
190-193
,共4页
约束性%多分类%混沌%粒子群优化
約束性%多分類%混沌%粒子群優化
약속성%다분류%혼돈%입자군우화
restrictive%multi-classes classification%chaos%Particle Swarm Optimization(PSO)
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.
約束性多分類問題是在某些工程和生產領域中存在的一類具有特殊約束條件的多分類模式識彆問題.針對傳統的有鑑督分類法無法解決約束性多分類問題,提齣一種基于混沌離散粒子群優化的約束性多分類模型(CBPSO-RMCM),併將該模型應用于盾構隧道管片選型預測.倣真實驗錶明,CBPSO-RMCM模型能有效地實現約束性多分類模式識彆,併且分類準確率較高.
약속성다분류문제시재모사공정화생산영역중존재적일류구유특수약속조건적다분류모식식별문제.침대전통적유감독분류법무법해결약속성다분류문제,제출일충기우혼돈리산입자군우화적약속성다분류모형(CBPSO-RMCM),병장해모형응용우순구수도관편선형예측.방진실험표명,CBPSO-RMCM모형능유효지실현약속성다분류모식식별,병차분류준학솔교고.
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.