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Fuzzy modeling applied to optical and surface properties of a ferrocenylsiloxane polyamide solution

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Central European Journal of Chemistry

Abstract

A fuzzy model was designed to predict changes in surface tension and maximum absorbance due to self-assembly in a DMF solution of poly{1,1′-ferrocene-diamide-[1,3-bis(propylene) tetramethyl-disiloxane} as a function of temperature and concentration. The building of fuzzy rule-based inference systems appears as a grey-box because it allows interpretation of the knowledge contained in the model as well as its improvement with a-priori knowledge. The method provides accurate results and increases the efficiency of utilizing the available information in the model. Small mean squared errors (0.0064 for absorbance and 0.79 for surface tension) and strong correlations between experiment and simulated results (0.93 and 0.97, respectively) were found during model validation. The results showed that it is feasible to apply a Mamdani fuzzy inference system to the estimation of optical and surface properties of a ferrocenylsiloxane polyamide solution.

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References

  1. R.R. Yager, D.P. Filev, Essentials of Fuzzy Modeling and Control (John Wiley & Sons., New York, 1994)

    Google Scholar 

  2. K. Tanaka, An Introduction to Fuzzy Logic for Practical Applications( Springer-Verlag, New York, 1997)

    Google Scholar 

  3. M.R. Sarmasti Emami, The Journal of Mathematics and Computer Science 1, 339 (2010)

    Google Scholar 

  4. D.A. Ress, JOM-e 51(8) (1999)

  5. M.N. Nádson Lima et al., AICHE 56, 965 (2010)

    Google Scholar 

  6. T. Hanai et al., Comput. Chem. Eng. 27, 1011 (2003)

    Article  CAS  Google Scholar 

  7. A. Altinten et al., Comput. Chem. Eng. 27, 1031 (2003)

    Article  CAS  Google Scholar 

  8. N.M.N. Lima et al. J. Appl. Polym. Sci.106, 981 (2007)

    Article  CAS  Google Scholar 

  9. B. Alonso et al., J. Organomet. Chem. 637, 642 (2001)

    Article  Google Scholar 

  10. I. Manners, J. Polym. Sci. Part. A. Polym. Chem. 40, 179 (2002)

    Article  CAS  Google Scholar 

  11. R. Resendes, et al., Chem. Eur. J. 7, 2414 (2001)

    Article  CAS  Google Scholar 

  12. A. Shimojima, K. Kuroda, Chem. Rec. 6, 53 (2006)

    Article  CAS  Google Scholar 

  13. G. Belorgey, G. Sauvet, In: W. Ando, R.G. Jones, J. Chojnowski (Ed.), Organosilicone block and graft copolymers, in Silicon-Containing Polymers: The Science and Technology of Their Synthesis (Springer, Kluwer Academic Publishers, 2000) 43

  14. H. Wang, M.A. Winnik, I. Manners, Macromolecules 40, 3784 (2007)

    Article  CAS  Google Scholar 

  15. G. Riess, Prog. Polym. Sci. 28, 1107 (2003)

    Article  CAS  Google Scholar 

  16. M. Cazacu, C. Racles, M. Alexandru, A. Ioanid, A. Vlad, Polym. Intern. 58(6), 697 (2009)

    Article  CAS  Google Scholar 

  17. M. Cazacu et al., J. Polym. Sci. Polym. Chem. 47(21), 5845 (2009)

    Article  CAS  Google Scholar 

  18. M. Cazacu et al., J. Optoelectron. Adv. Mater. 12, 294 (2010)

    CAS  Google Scholar 

  19. H. J. Coles et al., J. Mater. Chem. 9, 1085 (1999)

    Article  CAS  Google Scholar 

  20. M. Cazacu et al., Macromolecules 39, 3786 (2006)

    Article  CAS  Google Scholar 

  21. A. Kandel, Fuzzy expert systems (CRC Press, Inc., Boca Raton, FL, 1992)

    Google Scholar 

  22. M. Sugeno, G.T. Kang, Fuzzy. Set. Syst. 28, 15 (1988)

    Article  Google Scholar 

  23. B. Kosko, Neural networks and fuzzy systems: a dynamical system approach (Prentice Hall, Upper Saddle River, NJ, 1991)

    Google Scholar 

  24. H. Cartwright, Using artificial intelligence in chemistry and biology (CRC Press, New York, 2008)

    Book  Google Scholar 

  25. C.A. Pena Reyes, Coevolutionary Fuzzy Modeling (Springer Verlag, Berlin, 2004)

    Book  Google Scholar 

  26. S. Guillaume, IEEE T. Fuzzy. Syst. 9(3), 426 (2001)

    Article  Google Scholar 

  27. E.H. Mamdani, S. Assilian, Int. J. Hum-Comput. S. 7(1), 1 (1975)

    Google Scholar 

  28. M. Negnevitsky, Artificial Intelligence — A Guide to Intelligent Systems, 2nd edition (Addison Wesley, Essex, 2005)

    Google Scholar 

  29. T. Takagi, M. Sugeno, IEEE T. Syst. Man Cy. A. 15, 116 (1985)

    Google Scholar 

  30. J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-fuzzy and soft computing: a computational approach and machine intelligence (Prentice-Hall, Inc. Upper Saddle River, NJ 07458, 1997)

    Google Scholar 

  31. J.R. Lopes, W. Loh, Langmuir 14(4), 750 (1998)

    Article  CAS  Google Scholar 

  32. E. Cox, The Fuzzy Systems Handbook: A Practitioner’s Guide to Building, Using, and Maintaining Fuzzy Systems, 2nd edition (Academic Press, San Diego, CA, 1999)

    Google Scholar 

Download references

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Correspondence to Silvia Curteanu.

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Pislaru, M., Curteanu, S. & Cazacu, M. Fuzzy modeling applied to optical and surface properties of a ferrocenylsiloxane polyamide solution. cent.eur.j.chem. 10, 194–204 (2012). https://doi.org/10.2478/s11532-011-0126-3

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  • DOI: https://doi.org/10.2478/s11532-011-0126-3

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