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A neuro-coevolutionary genetic fuzzy system to design soft sensors

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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.

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Notes

  1. HYSYS 3.0, HYPROTECH Ltd.

  2. Other values did not improved the performance without increasing so hard the computational cost.

  3. Running in a computer AMD-64 3200 2 GHz, 2 GB of RAM

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Acknowledgments

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

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Authors and Affiliations

  1. Graduate School in Electrical Engineering and Applied Computer Sciences, Federal University of Technology-Paraná, Curitiba, PR, Brazil

    Myriam Regattieri Delgado, Elaine Yassue Nagai & Lúcia Valéria Ramos de Arruda

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  1. Myriam Regattieri Delgado
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  2. Elaine Yassue Nagai
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  3. Lúcia Valéria Ramos de Arruda
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Correspondence to Myriam Regattieri Delgado.

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Delgado, M.R., Nagai, E.Y. & de Arruda, L.V.R. A neuro-coevolutionary genetic fuzzy system to design soft sensors. Soft Comput 13, 481–495 (2009). https://doi.org/10.1007/s00500-008-0363-3

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