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Status Evaluation of Loose of Jig Bed Based on Fuzzy Inference System 被引量:1

Status Evaluation of Loose of Jig Bed Based on Fuzzy Inference System
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摘要 This paper mainly describes that loose of jig bed affects jig's separation effect, and the corresponding fuzzy rules were built. Using the evaluating index of jig's separation effect--imperfection (I) and total misplaced material (Cz), it evaluates status of loose of jig bed by fuzzy inference system. Experimental simulation and applications in practice prove the method's feasibility. This paper mainly describes that loose of jig bed affects jig's separation effect, and the correspondingfuzzy rules were built. Using the evaluating index of jig's separation effect——imperfection (I) and total misplacedmaterial (Cz), it evaluates status of loose of jig bed by fuzzy inference system. Experimental simulation and applications in practice prove the method's feasibility.
出处 《Journal of China University of Mining and Technology》 2003年第2期142-144,共3页 中国矿业大学学报(英文版)
关键词 JIG loose of jig bed fuzzy inference system 跳汰床层 模糊推理系统 跳汰机 状态评估
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  • 1MCAVOY T J.Contemplative stance for chemical process[J].Automatic,1992,28 (2):441-442.
  • 2ASSIS A D J,FILHO R M.Soft sensor development for on-line bioreactor state estimation[J].Computer and Chemical Engineering,2000,24:1099-1103.
  • 3CORTES C,VAPNIK V N,Support-vector networks[J].Machine Learning,1995,20(1):273-297.
  • 4CHEN S,BILLINGS S A.Neural networks for nonlinear dynamic system modeling and identification[J].International Journal of Control,2000,12(6):685-687.
  • 5CHIU S L.Fuzzy model identification based on duster estimation[J].Journal of Intelligent and Fuzzy Systems,1994,2(3):267-278.
  • 6JANG J S R.ANFIS:adaptive-network-based fuzzy inference system[J].IEEE Transactions on System,Man and Cybernetics,June 1993,23:665-685.
  • 7LEE S J,OUYANG C S.A neuro-fuzzy system modeling with self-constructing rule generation and hybrid SVD-based learning[J].IEEE Transactions on Fuzzy System,2003,11(3):341-353.
  • 8AZEEM M F,HANMANDLU M,AHMAD H.Generalization of adaptive neuro-fuzzy inference systems[J].IEEE Transactions on Neural Networks,2000,11(6):1332-1346.
  • 9倪志伟,蔡庆生,方瑾.用神经网络来挖掘数据库中的关联规则[J].系统仿真学报,2000,12(6):685-687. 被引量:10
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