Skip to main content
Springer Nature Link
Log in

A New Strategy for Parameter Estimation of Dynamic Differential Equations Based on NSGA II

  • Conference paper
Simulated Evolution and Learning (SEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4247))

Included in the following conference series:

Abstract

A new strategy for parameter estimation of dynamic differential equations based on nondominated sorting genetic algorithm II (NSGA II) and one-step-integral Treanor algorithm is presented. It is adopted to determine the exact model of catalytic cracking of gas oil. Compared with those conventional methods, for example, quadratic programming, the method proposed in this paper is more effective and feasible. With the parameters selected from the NSGA II pareto-optimal solutions, more accurate results can be obtained.

This work was supported by the National Natural Science Foundation of China (No. 20206027), the Key Technologies R&D Program in the 10th Five-year Plan of China (No. 2004BA210A01), and the Technologies R&D Programs of Zhejiang Province (No. 2006C31051 and No. 2006C33059).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Jiang, A.P., Shao, Z.J., Qian, J.X.: Optimization of Reaction Parameters Based on rSQP and Hybrid Automatic Differentiation Algorithm. Journal of Zhejiang University (Engineering Science) 38, 1606–1610 (2004)

    Google Scholar 

  2. Tjoa, I.-B., Biegler, L.T.: Simultaneous Solution and Optimization Strategies for Parameter Estimation of Differential-algebraic Equations Systems. Ind. Eng. Chem. Res. 30, 376–385 (1991)

    Article  Google Scholar 

  3. Srinivas, N., Deb, K.: Multiobjective Function Optimization Using Nondominated Sorting Genetic Algorithms [J]. Evolutionary Computation 2(3), 221–248 (1995)

    Article  Google Scholar 

  4. Goldberg, D.E.: Genetic Algorithm in Search, Optimization and Machine Learning [M]. Addison-Wesley, Reading (1989)

    Google Scholar 

  5. Deb, K., Agrawal, S., Pratap, A., et al.: A Fast Elitist Nondominated Sorting Genetic Algorithm For Multi-objective Optimization: NSGA II [A]. In: Proc of the Parallel Problem Solving from Nature VI Conf. [C], Paris, pp. 849–858 (2000)

    Google Scholar 

  6. Xu, S.L.: Common Algorithm Set by FORTRAN. Tsinghua University Press (1995)

    Google Scholar 

  7. Froment, G.F., Bischoff, K.B.: Chemical Reactor Analysis and Design. Wiley, New York (1979)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Education Science, Hangzhou Teachers College, Hangzhou, 310036, China

    Yingzi Shi

  2. National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, China

    Jiangang Lu & Qiang Zheng

Authors
  1. Yingzi Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiangang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiang Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC

    Tzai-Der Wang

  2. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  3. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  4. Department of Computer Science and Technology, University of Sci. & Tech. of China, 230026, Hefei, Anhui, P.R. China

    Xufa Wang

  5. School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

    Hussein Abbass

  6. The Univertity of Tokyo, Japan

    Hitoshi Iba

  7. Department of Computer, University of Science and Technology of China, 230027, Hefei, China

    Guo-Liang Chen

  8. University of Birmingham, Birmingham, UK

    Xin Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Shi, Y., Lu, J., Zheng, Q. (2006). A New Strategy for Parameter Estimation of Dynamic Differential Equations Based on NSGA II. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_44

Download citation

  • DOI: https://doi.org/10.1007/11903697_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47331-2

  • Online ISBN: 978-3-540-47332-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics