Algorithm for generating interpretable fuzzy controllers: an application to a pressure process
Keywords:
Fuzzy identification, Interpretability, Fuzzy controller, Process pressure
Abstract
A novel approach for the development of linguistically interpretable fuzzy singleton models from experimental data is proposed. The proposed methodology uses triangular sets with 0.5 interpolations. Averaging operator, instead of T-norm operator, is used for combining fuzzy rules. Singleton consequents are employed and least square method is used to adjust the consequents. The most promissory aspect in our proposal consists in achieving model without sacrificing the fuzzy system interpretability. The real-world applicability of the proposed approach is demonstrated by application to a pressure control using the LabVolt Process Control Training System (6090).
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References
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[2] Sugeno, M., Yasukawa, T., “A fuzzy logic based approach to qualitative modeling”. Transactions on Fuzzy Systems, vol. 1, No. 1, pp. 7-31. 1993
[3] Bezdek J. C. (1987). Pattern recognition with Fuzzy Objective Function Algorithms. Ed. Plenum Press.
[4] Guztafson E. E., Kessel W. C. Fuzzy Clustering with a Fuzzy Covariance Matrix. IEEE CDC, San Diego, California, pp. 503 – 516.1979.
[5] Nauck, D., Kruse, R., “Nefclass - a neuro-fuzzy approach for the classification of data”, In Proceedings of the Symposium on Applied Computing, 1995.
[6] Nauck, D., Kruse, R., “Neuro-fuzzy systems for function approximation”. Fuzzy Sets and System. 101(2), pp. 261-271. Jan. 1999.
[7] Espinosa, J., Vandewalle, J., “Constructing fuzzy models with linguistic integrity form numerical data-afreli algorithm”, IEEE Trans. Fuzzy Systems, vol. 8, pp. 591 – 600, Oct. 2000.
[8] Sala, A. (1998). Validación y Aproximación Funcional en Sistemas de Control Basados en Lógica Borrosa. Universidad Politécnica de Valencia. Tesis Doctoral.
[9] Sala, A., Albertos, P., Inference error minimisation: fuzzy modelling of ambiguous functions. Fuzzy Sets and Systems, 121 (2001) pp. 95 – 111. 2001
[10] Díez J. L., Navarro J. L., Sala A. (2004). Algoritmos de Agrupamiento en la Identificación de Modelos Borrosos. RIAI: Revista Iberoamericana de Automática e Informática Industrial.
[11] Paiva, R. P., Dourado, A., “Interpretability and learning in neuro-fuzzy systems”, Fuzzy Sets and System. 147(2004), pp. 17-38. 2004.
[12] Contreras, J., Misa, R., Paz, J., Building Interpretable Fuzzy Systems: A New Methodology. In proceedings of Electronics, Robotics and Automotive Mechanics Conference CERMA 2006. IEEE Computer Society. Pp.: 172-178. Nov. 2006.
Published
2011-12-12
How to Cite
Contreras Montes, J., & Muñoz Aldana, D. (2011). Algorithm for generating interpretable fuzzy controllers: an application to a pressure process. ITECKNE, 8(2), 177-182. https://doi.org/https://doi.org/10.15332/iteckne.v8i2.2734
Issue
Section
Research and Innovation Articles
