Cell Mapping Model for Complex Systems with Statistical Trajectory Based on Data Mining
作者单位：School of Automation and Electrical EngineeringUniversity of Science &Technology Beijing Department of mathematicsChang Zhi College
会议名称：《第37届中国控制会议》
会议日期：2018年
学科分类：0711[理学-系统科学] 07[理学] 071101[理学-系统理论]
关 键 词：Statistical law ISODATA Pattern class variable IQPSO Cell mapping
摘 要：This paper proposes a new type of modeling framework for complex *** kind of systems has the characteristics:it is difficult to establish the mechanism model and its dynamics obey the statistical ***,in order to extract the statistical attributes,an iterative self-organizing data analysis techniques algorithm（ISODATA）of data mining is used to separate process data into a number of *** on these classes,a pattern class space in which each class is taken as an element is ***,to describe the system dynamic with statistical law,a pattern class variable is defined in the pattern class *** further solve the incalculable problem of the pattern class variable,a cell space is introduced where each pattern class variable is measured by cell *** on this measure,an improved controlled autoregressive moving average（CARMA） model described by the pattern class variable is obtained,and model parameters are identified by an improved quantum-behaved particle swarm optimization（IQPSO） *** attractive of the algorithm lies in enhancing the ability of the global development and local *** on the model,the corresponding cell mapping model is derived by the state space representation,which avoid the expansion of ***,the global performance of the pattern class space is analyzed via cell ***,a simulation in the dynamic trajectory of a sintering process is given to demonstrate the feasibility of the proposed approach.