DocumentCode :
554129
Title :
Chaotic differential evolution algorithm based on mixed method for large-scale industrial processes of fuzzy model
Author :
Dakuo He ; Yuanyuan Zhao ; Lifeng Wang ; Hongrui Chang
Author_Institution :
Sch. of Inf. Sci. & Eng., State Key Lab. of Integrated Autom. for Process Ind., Northeastern Univ., Shenyang, China
Volume :
3
fYear :
2011
fDate :
26-28 July 2011
Firstpage :
1381
Lastpage :
1385
Abstract :
In the large-scale industrial processes, there are more slow perturbations. So the mathematical model of an actual system is difficult to be accurate. When optimizing large-scale industrial processes, the mathematical model and the actual system does not match, that is model-reality difference. In order to deal with this problem, the structure of decomposition and coordination is used in this paper. The whole large-scale industrial processes can be decomposed into several subprocesses that are interactive, then the differential evolution algorithm (shorts for DE) is introduced to solve the submodel, and the mixed method including open-loop mixed method and the mixed method with global feedback is the coordinate strategy used in this paper. Chaotic differential evolution algorithm based on mixed method for large-scale industrial processes of fuzzy model is proposed in this paper. A classical example of large-scale industrial processes is applied and the simulation results show the validity of the method.
Keywords :
chaos; evolutionary computation; feedback; fuzzy set theory; large-scale systems; manufacturing processes; open loop systems; chaotic differential evolution algorithm; fuzzy model; global feedback; large-scale industrial process; mathematical model; model reality difference; open loop mixed method; Data models; Educational institutions; Helium; Mathematical model; Programming; Simulation; Steady-state; chaotic differential evolution algorithm; fuzzy model; fuzzy nonlinear programming; large-scale industrial processes; mixed method;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
ISSN :
2157-9555
Print_ISBN :
978-1-4244-9950-2
Type :
conf
DOI :
10.1109/ICNC.2011.6022329
Filename :
6022329
Link To Document :
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