A generalized Markov chain model based on generalized interval probability  

作　　者：XIE FengYun WU Bo HU YouMin WANG Yan 

机构地区：[1]State Key Laboratory for Digital Manufacturing Equipment and Technology, Huazhong University of Science& Technology [2]School of Mechanical and Electronical Engineering, East China Jiaotong University [3]Woodruff of School of Mechanical Engineering, Georgia Institute of Technology

出　　处：《Science China(Technological Sciences)》2013年第9期2132-2136,共5页中国科学（技术科学英文版）

基　　金：supported by the National Key Basic Research Program of China (973 Program) (Grant No. 2011CB706803);the National Natural Science Foundation of China (Grant Nos. 51175208, 51075161)

摘　　要：In the traditional Markov chain model (MCM), aleatory uncertainty because of inherent randomness and epistemic uncertainty due to the lack of knowledge are not differentiated. Generalized interval probability provides a concise representation for the two kinds of uncertainties simultaneously. In this paper, a generalized Markov chain model (GMCM), based on the generalized interval probability theory, is proposed to improve the reliability of prediction. In the GMCM, aleatory uncertainty is represented as probability; interval is used to capture epistemic uncertainty. A case study for predicting the average dynamic compliance in machining processes is provided to demonstrate the effectiveness of proposed GMCM. The results show that the proposed GMCM has a better prediction performance than that of MCM.

关 键 词：UNCERTAINTY generalized interval probability generalized Markov chain model （GMCM） PREDICTION 

分 类 号：O211.62[理学—概率论与数理统计]