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author:

Zhao, C. (Zhao, C..) [1] | An, A. (An, A..) [2] | Xu, Q. (Xu, Q..) [3]

Indexed by:

Scopus

Abstract:

The determination of the optimal model parameters for kinetic systems development of kinetic models is a time consuming, iterative process [1]. In this paper, we presented a novel hybrid Differential Evolution (DE) algorithm for solving kinetic parameter estimation problems based on the Differential Evolution technique together with a local search strategy. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. The computational results indicate that the global searching ability and the convergence speed of this hybrid algorithm are significantly improved. Additionally, study of kinetic model parameters for an irreversible, first-order reaction system was carried out to test the applicability of the proposed algorithm. The suggested method can be used to estimate suitable values for the model parameters of a complex mathematical model. © 2012 IEEE.

Keyword:

Guass-Newton method; Hybrid Differential Evolution (HDE); Kinetic models; Parameter estimation

Community:

  • [ 1 ] [Zhao, C.]Faculty of College of Chemistry and Chemical Engineering, FuZhou University, FuZhou, 350108, China
  • [ 2 ] [An, A.]Faculty of School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou, Gansu 730050, China
  • [ 3 ] [Xu, Q.]Faculty of College of Chemistry and Chemical Engineering, FuZhou University, FuZhou, 350108, China

Reprint 's Address:

  • [Zhao, C.]Faculty of College of Chemistry and Chemical Engineering, FuZhou University, FuZhou, 350108, China

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Source :

Proceedings of the 2012 24th Chinese Control and Decision Conference, CCDC 2012

Year: 2012

Page: 1696-1700

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

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Chinese Cited Count:

30 Days PV: 0

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