A neuro-coevolutionary genetic fuzzy system to design soft sensors

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.

[1]  Luigi Fortuna,et al.  Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control) , 2006 .

[2]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[3]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[4]  Joos Vandewalle,et al.  Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm , 2000, IEEE Trans. Fuzzy Syst..

[5]  H. Ishibuchi,et al.  Genetic-algorithm-based approach to linguistic approximation of nonlinear functions with many input variables , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[6]  M. Sugeno,et al.  Derivation of Fuzzy Control Rules from Human Operator's Control Actions , 1983 .

[7]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[8]  Fernando José Von Zuben,et al.  Coevolutionary genetic fuzzy systems: a hierarchical collaborative approach , 2004, Fuzzy Sets Syst..

[9]  Alex Arenas,et al.  Neural virtual sensor for the inferential prediction of product quality from process variables , 2002 .

[10]  Yaochu Jin,et al.  Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement , 2000, IEEE Trans. Fuzzy Syst..

[11]  Raúl Pérez,et al.  Completeness and consistency conditions for learning fuzzy rules , 1998, Fuzzy Sets Syst..

[12]  W. Pedrycz,et al.  An introduction to fuzzy sets : analysis and design , 1998 .

[13]  Fernando Gomide,et al.  Hierarchical Genetic Fuzzy Systems: Accuracy, Interpretability and Design Autonomy , 2003 .

[14]  Samuel Kaski,et al.  Comparing Self-Organizing Maps , 1996, ICANN.

[15]  Hui Shao,et al.  Developing soft sensors using hybrid soft computing methodology: a neurofuzzy system based on rough set theory and genetic algorithms , 2006, Soft Comput..

[16]  Seppo J. Ovaska,et al.  Industrial applications of soft computing: a review , 2001, Proc. IEEE.

[17]  Kenneth A. De Jong,et al.  Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.

[18]  Richard S. Sutton,et al.  Neural networks for control , 1990 .

[19]  H. Ishibuchi.  Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases , 2004 .

[20]  R M Ansari and M O Tadé.  Nonlinear Model-based Process Control: Applications in Petroleum Refining , 2011 .

[21]  Lúcia Valéria Ramos de Arruda,et al.  Soft sensor based on fuzzy model identification , 2005 .

[22]  Flávio Neves,et al.  Startup of a distillation column using intelligent control techniques , 2005, Comput. Chem. Eng..

[23]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[24]  Risto Miikkulainen,et al.  Forming Neural Networks Through Efficient and Adaptive Coevolution , 1997, Evolutionary Computation.

[25]  Eduardo D. Sontag,et al.  Neural Networks for Control , 1993 .

[26]  Carlos E. García,et al.  Fundamental Process Control , 1988 .