Advertisement

Intuitionistic Fuzzy Logic Implementation to Assess Purposeful Model Parameters Genesis

  • Tania PenchevaEmail author
  • Maria Angelova
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 657)

Abstract

In this investigation, intuitionistic fuzzy logic is implemented to derive intuitionistic fuzzy estimations of model parameters of yeast fed-batch cultivation. Two kinds of simple genetic algorithms with operators sequences selection-crossover-mutation and mutation-crossover-selection are here considered, both applied for the purposes of parameter identification of S. cerevisiae fed-batch cultivation. Intuitionistic fuzzy logic overbuilds the results achieved by the application of recently developed purposeful model parameters genesis procedure in order to keep promising results obtained. Behavior of applied algorithms has also been examined at different values of the genetic algorithms parameter generation gap, proven as the most sensitive parameter toward convergence time. Results obtained after the implementation of intuitionistic fuzzy logic for the assessment of algorithms performances have been compared and based on the evaluations in each case the most reliable algorithm has been distinguished.

Keywords

Intuitionistic fuzzy logic Genetic algorithm Parameter identification Fermentation process Saccharomyces cerevisiae 

References

  1. 1.
    Adeyemo, J., Enitian, A.: Optimization of fermentation processes using evolutionary algorithms—a review. Sci. Res. Ess. 6(7), 1464–1472 (2011)Google Scholar
  2. 2.
    Angelova, M., Pencheva, T.: Tuning genetic algorithm parameters to improve convergence time. Int. J. Chem. Eng., article ID 646917 (2011). http://www.hindawi.com/journals/ijce/2011/646917/cta/
  3. 3.
    Angelova, M., Atanassov, K., Pencheva, T.: Intuitionistic fuzzy estimations of purposeful model parameters genesis. In: IEEE 6th International Conference “Intelligent Systems”, Sofia, Bulgaria, 6–8 Sept 2012, pp. 206–211 (2012)Google Scholar
  4. 4.
    Angelova, M., Atanassov, K., Pencheva, T.: Intuitionistic fuzzy logic based quality assessment of simple genetic algorithm, vol. 2. In: Proceedings of the 16th International Conference on System Theory, Control and Computing (ICSTCC), 12–14 Oct 2012, Sinaia, Romania, Elecrtonic edition (2012)Google Scholar
  5. 5.
    Angelova, M., Tzonkov, S., Pencheva, T.: Genetic algorithms based parameter identification of yeast fed-batch cultivation. Lecture Notes in Computer Science, vol. 6046, pp. 224–231 (2011)Google Scholar
  6. 6.
    Angelova, M., Pencheva, T.: Algorithms improving convergence time in parameter identification of fed-batch cultivation. Comptes Rendus de l’Académie Bulgare des Sciences 65(3), 299–306 (2012)zbMATHGoogle Scholar
  7. 7.
    Angelova, M., Pencheva, T.: Purposeful model parameters genesis in simple genetic algorithms. Comput. Math Appl. 64, 221–228 (2012)CrossRefGoogle Scholar
  8. 8.
    Atanassov, K.: Intuitionistic Fuzzy Sets. Springer, Heidelberg (1999)CrossRefzbMATHGoogle Scholar
  9. 9.
    Atanassov, K.: On intuitionistic Fuzzy Sets Theory. Springer, Berlin (2012)CrossRefzbMATHGoogle Scholar
  10. 10.
    Chipperfield, A.J., Fleming, P., Pohlheim, H., Fonseca, C.M.: Genetic algorithm toolbox for use with MATLAB, User’s guide, version 1.2. Department of Automatic Control and System Engineering, University of Sheffield, UK (1994)Google Scholar
  11. 11.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wiley Publishing Company, Massachusetts (1989)zbMATHGoogle Scholar
  12. 12.
    Jones, K.: Comparison of genetic algorithms and particle swarm optimization for fermentation feed profile determination. In: Proceedings of the CompSysTech’2006, 15–16 June 2006, Veliko Tarnovo, Bulgaria, IIIB.8-1–IIIB.8-7 (2006)Google Scholar
  13. 13.
    Pencheva, T., Angelova, M., Atanassov, K.: Intuitionistic fuzzy logic implementation to assess genetic algorithms quality. Comput. Math. Appl. (2012)Google Scholar
  14. 14.
    Pencheva, T., Roeva, O., Hristozov, I.: Functional state approach to fermentation processes modelling. In: Tzonkov, St., Hitzmann, B. (eds.) Prof. Marin Drinov Academic Publishing House, Sofia (2006)Google Scholar
  15. 15.
    Roeva, O. (ed.): Real-world application of genetic algorithms. InTech (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Institute of Biophysics and Biomedical EngineeringBulgarian Academy of SciencesSofiaBulgaria

Personalised recommendations