Soft Computing

, Volume 13, Issue 5, pp 481–495 | Cite as

A neuro-coevolutionary genetic fuzzy system to design soft sensors

  • Myriam Regattieri Delgado
  • Elaine Yassue Nagai
  • Lúcia Valéria Ramos de Arruda
Focus

Abstract

This paper addresses a soft computing-based approach to design soft sensors for industrial applications. The goal is to identify second-order Takagi–Sugeno–Kang fuzzy models from available input/output data by means of a coevolutionary genetic algorithm and a neuro-based technique. The proposed approach does not require any prior knowledge on the data-base and rule-base structures. The soft sensor design is carried out in two steps. First, the input variables of the fuzzy model are pre-selected from the secondary variables of a dynamical process by means of correlation coefficients, Kohonen maps and Lipschitz quotients. Such selection procedure considers nonlinear relations among the input and output variables. Second, a hierarchical coevolutionary methodology is used to identify the fuzzy model itself. Membership functions, individual rules, rule-bases and fuzzy inference parameters are encoded into each hierarchical level and a shared fitness evaluation scheme is used to measure the performance of individuals in such levels. The proposed methodology is evaluated by developing soft sensors to infer the product composition in petroleum refining processes. The obtained results are compared with other benchmark approaches, and some conclusions are presented.

Keywords

Takagi–Sugeno–Kang fuzzy models Kohonen maps Coevolution Genetic algorithms Soft sensing Industrial dynamical processes 

References

  1. Ansari RM, Tadé M (2000) Non-linear model-based process control: applications in petroleum refining. Springer, HeidelbergGoogle Scholar
  2. Cordón O, Herrera F, Hoffmann F, Magdalena L (eds) (2001) Genetic fuzzy systems. Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, New YorkGoogle Scholar
  3. Delgado MR, Zuben FV (2003) Interpretability issues in fuzzy modeling—studies in fuzziness and soft computing. In: Hierarchical genetic fuzzy systems: accuracy, interpretability and design autonomy. Physica-Verlag, New York, pp 379–405Google Scholar
  4. Delgado MR, Zuben FV, Gomide F (2000) Optimal parameterization of evolutionary Takagi–Sugeno fuzzy systems. In: Proceedings of 8th IPMU00, pp 650–657Google Scholar
  5. Delgado MR, Zuben FV, Gomide F (2004) Coevolutionary genetic fuzzy systems: a hierarchical collaborative approach. Fuzzy Sets Syst 141:89–106MATHCrossRefGoogle Scholar
  6. Dote Y, Ovaska SJ (2001) Industrial applications of soft computing: a review. Proc IEEE 89:1243–1265Google Scholar
  7. Espinosa J, Vandewalle J (2000) Constructing fuzzy models with linguistic integrity from numerical data-afreli algorithm. IEEE Trans Fuzzy Syst 8:591–600CrossRefGoogle Scholar
  8. Fabro JA, Arruda LVR, Neves-Jr F (2005) Startup of a distillation column using intelligent control techniques. Comput Chem Eng 30:309–320Google Scholar
  9. Fletcher R (1987) Practical methods of optimization. Wiley, New YorkGoogle Scholar
  10. Fortuna L, Granziani S, Rizzo A, Xibilia MG (2007) Soft sensors for monitoring and control of industrial processes. Springer, HeidelbergGoogle Scholar
  11. Gonzales A, Perez R (1998) Completeness and consistency conditions for learning fuzzy rules. Fuzzy Sets Syst 96:37–51CrossRefGoogle Scholar
  12. Ishibuchi H, Nakashima T (1999) Genetic-algorithm-based approach to linguistic approximation of nonlinear functions with many input variables. In: Proceedings of FUZZ-IEEE’99, Seoul, Korea, pp 779–784Google Scholar
  13. Jang JSR (1993) Anfis: Adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–684CrossRefGoogle Scholar
  14. Jin Y (2000) Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement. IEEE Trans Fuzzy Syst 8:212–221CrossRefGoogle Scholar
  15. Kaski S, Lagus K (1996) Comparing self-organizing maps. In: Proceedings of ICANN96, Bochum, Germany, pp 809–814Google Scholar
  16. Klement E, Mesiar R, Pap E (2000) Triangular norms. Kluwer, DordrechtGoogle Scholar
  17. Luo JX, Shao HH (2006) Developing soft sensors using hybrid soft computing methodology: a neurofuzzy system based on rough set theory and genetic algorithms. Soft Comput 10:54–60CrossRefGoogle Scholar
  18. Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. Springer, HeidelbergGoogle Scholar
  19. Moriarty D, Miikkulainen R (1997) Forming neural networks through efficient and adaptive coevolution. Evol Comput 5:373–399CrossRefGoogle Scholar
  20. Nagai EY (2006) Automatic identification of inferential fuzzy models (in portuguese). PhD thesis, Federal University of Techonology-ParanaGoogle Scholar
  21. Nagai EY, Arruda LVR (2005) Soft sensor based on fuzzy model identification. In: Proceedings of 16th IFAC World Congress, Prague, Czech RepublicGoogle Scholar
  22. Pedrycz W, Gomide F (1998) An introduction to fuzzy sets: analysis and design. MIT Press, CambridgeGoogle Scholar
  23. Potter M, Jong KD (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8:1–29CrossRefGoogle Scholar
  24. Prett D, Garcia C (1998) Fundamental process control. Butterworth, LondonGoogle Scholar
  25. Qin SJ (1996) Neural networks for control. In: Neural networks for intelligent sensor and control—practical issues and some solutions. Academic Press, New York, pp 215–236Google Scholar
  26. Rallo R, Ferre-Gine J, Arenas A, Giralt F (2002) Neural virtual sensor for the inferential prediction of product quality from process variables. ensor for the inferential prediction of product quality from process variables. Comput Chem Eng 26:1735–1754CrossRefGoogle Scholar
  27. Takagi T, Sugeno M (1983) Derivation of fuzzy control rules from human operator’s control actions. In: Proceedings of the IFAC symposium on fuzzy information, knowledge representation and decision analysis, Marseilles, France, pp 55–60Google Scholar
  28. Zadeh L (1965) Fuzzy sets. Inf Control 8:338–352MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Myriam Regattieri Delgado
    • 1
  • Elaine Yassue Nagai
    • 1
  • Lúcia Valéria Ramos de Arruda
    • 1
  1. 1.Graduate School in Electrical Engineering and Applied Computer SciencesFederal University of Technology-ParanáCuritibaBrazil

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