王磊

个人信息Personal Information

教授

博士生导师

硕士生导师

主要任职:数学科学学院副院长

其他任职:石河子大学理学院院长(援疆)

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:数学科学学院

学科:运筹学与控制论

电子邮箱:wanglei@dlut.edu.cn

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Modelling and parameter identification of a nonlinear enzyme-catalytic time-delayed switched system and its parallel optimization

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论文类型:期刊论文

第一作者:Yuan, Jinlong

通讯作者:Yuan, JL (reprint author), Dalian Univ Technol, Sch Math Sci, Dalian 116024, Liaoning, Peoples R China.

合写作者:Wang, Lei,Xie, Jun,Zhang, Xu,Feng, Enmin,Yin, Hongchao,Xiu, Zhilong

发表时间:2016-10-01

发表刊物:APPLIED MATHEMATICAL MODELLING

收录刊物:SCIE、EI、Scopus

卷号:40

期号:19-20

页面范围:8276-8295

ISSN号:0307-904X

关键字:Parameter identification; Time-delayed switched system; Biological robustness; Hybrid time-scaling transformation and constraint transcription; Parallel PSO

摘要:In this paper, we propose a nonlinear enzyme-catalytic time-delayed switched system with unknown state-delays, switching times and system parameters for describing the process of batch culture of glycerol bioconversion to 1,3-propanediol (1,3-PD) induced by Klebsiella pneumoniae (K. pneumoniae). Some important properties of the time-delayed switched system are discussed. In consideration of the difficulty in accurately measuring the concentration of intracellular substances and the absence of equilibrium points for the time delayed switched system, we quantitatively define biological robustness of the intracellular substance concentrations for the entire process of batch culture. Our goal is to identify the unknown quantities. To this end, we formulate an optimization problem in which the cost function minimizes the defined biological robustness and the unknown state-delays, switching times and system parameters are regarded as decision variables. This optimization problem is subject to the time-delayed switched system, continuous state inequality constraints and parameter constraints. The hybrid time-scaling transformation, constraint transcription and local smoothing approximation techniques are used to convert this optimization problem to a sequence of approximate subproblems. In consideration of both the difficulty of finding analytical solutions and the highly complex nature of this optimization problem, we develop a parallel particle swarm optimization (PPSO) algorithm to solve these approximate subproblems. Finally, we explore the appropriateness of the optimal estimates for the state-delays, switching times and system parameters as well as the effectiveness of the parallel algorithm via numerical simulations. (C) 2016 Elsevier Inc. All rights reserved.