A neuro-coevolutionary genetic fuzzy system to design soft sensors

  title={A neuro-coevolutionary genetic fuzzy system to design soft sensors},
  author={Myriam Regattieri Delgado and Elaine Yassue Nagai and L{\'u}cia Val{\'e}ria Ramos de Arruda},
  journal={Soft Comput.},
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… CONTINUE READING
Highly Cited
This paper has 18 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-9 of 9 extracted citations


Publications referenced by this paper.
Showing 1-10 of 24 references

Interpretability issues in fuzzy modeling—studies in fuzziness and soft computing. In: Hierarchical genetic fuzzy systems: accuracy, interpretability and design autonomy

  • MR Delgado, FV Zuben
  • 2003
Highly Influential
8 Excerpts

Optimal parameterization of evolutionary Takagi–Sugeno fuzzy systems

  • MR Delgado, FV Zuben, F Gomide
  • Proceedings of 8th IPMU00,
  • 2000
Highly Influential
8 Excerpts

Soft sensors for monitoring and control of industrial processes

  • L Fortuna, S Granziani, A Rizzo, MG Xibilia
  • 2007
Highly Influential
9 Excerpts

Automatic identification of inferential fuzzy models (in portuguese)

  • EY Nagai
  • PhD thesis,
  • 2006
Highly Influential
6 Excerpts

Non-linear model-based process control: applications in petroleum refining

  • RM Ansari, M Tadé
  • 2000
Highly Influential
9 Excerpts

Fundamental process control

  • D Prett, C Garcia
  • 1998
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…