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Pattern Recognition Based Dynamics Description of Production Processes in Metric Spaces
Author(s) -
Wang Mushu,
Xu Zhengguang,
Guo Lingli
Publication year - 2017
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1471
Subject(s) - computer science , data mining , metric (unit) , system dynamics , algorithm , metric space , production (economics) , raw data , entropy (arrow of time) , pattern recognition (psychology) , mathematics , artificial intelligence , engineering , discrete mathematics , operations management , physics , quantum mechanics , economics , macroeconomics , programming language
A new type of modeling method is put forward based on pattern recognition (PR) technology for some industrial production processes. The proposed method is a pure data‐driven modeling method since the model is independent of the controlled plant, and it is based on the measured input and output (I/O) data of the controlled plant in a closed loop. Different from the traditional modeling method, the system dynamics is described by I/O classes, which are obtained from raw I/O data through partitioning of the data space respectively and I/O orders of the model resort to the conditional entropy. The covering algorithm based pattern classification (PC) is used to establish the mapping between input and output of the proposed model in metric spaces. The experimental results illustrate the feasibility of the modeling method.