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


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.


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



The authors would like to thank Brazilian Petroleum Agency(ANP/FINEP) for grant PRH-ANP/MCT-PRH10-UTFPR.


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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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