Fuzzy Predictive Model of the Vertical Acceleration of a High Speed Vessel in Pitch Motion

  • Francisco Gil Navia
  • Juan Contreras Montes


An adaptable fuzzy inference technique is being described in order to generate predictive models of the acceleration of the pitching of a high speed vessel, from the data obtained from the web on an experiment conducted by the University of Iowa. The geometry of interest in the experiment is a scale model of the type 1/46.6 of the DTMB model 5415 (DDG-51). The fuzzy algorithm for the generation of the predictive model uses a triangular partition with a 0.5 overlapping and consequents of the Singleton type. The consequents are adjusted in an automatic fashion by using recursive least squares. The algorithm shows a very low computational complexity rate which allows for it to be used for on line identification.


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BEZDEK J. C. Pattern recognition with Fuzzy Objective Function Algortithms. Ed. Plenum Press. 1987.

CONTRERAS, J. Introducción al Control Automático, Editorial Escuela Naval, Almirante Padilla, Cartagena, Colombia, 2006.

CONTRERAS, J., MISA, R., PAZ, J., Building Interpretable Fuzzy Systems: A New Approach to Fuzzy Modeling. En proceedings of Electronics, Robotics and Automotive Mechanics Conference CERMA 2006. IEEE Computer Society. Pags.: 172-178. 2006.

DÍEZ J. L., NAVARRO J. L., SALA A. Algoritmos de Agrupamiento en la Identificación de Modelos Borrosos. RIAI: Revista Iberoamericana de Automática e Informática Industrial. 2004.

ESPINOSA, J., VANDEWALLE, J. Constructing fuzzy models with linguistic integrity form numerical data-afreli algorithm, IEEE Trans. Fuzzy Systems, vol. 8, pp. 591 – 600. 2000.

ESPINOSA, J., VANDEWALLE, J., Wertz, V, Fuzzy Logic, Identification and Predictive Control. Springer. Estados Unidos. 2005.

GIL, F., CONTRERAS, J. Automatic tracking of Target: Application on a Prototype of Cannon. II Conferencia/Workshop de Vehículos/Sistemas No-Tripulados (UV/S) de América Latina. Panamá. Agosto de 2008.

GUILLAUME, S., CARNOMORDIC, B. Generating an interpretable Family of Fuzzy Partitions Form Data, IEEE Trans. Fuzzy Systems, vol. 12, No. 3, pp. 324 – 335. 2004.

GUZTAFSON E. E., KESSEL W. C. Fuzzy Clustering with a Fuzzy Covariance Matrix. IEEE CDC, San Diego, California, pp. 503 – 516. 1979.

IRVINE, M., LONGO, J., and STERN, F., Pitch and Heave Tests and Uncertainty Assessment for a Surface Combatant in Regular Head Waves, Journal of Ship Research, submitted. 2006.

NAUCK, D., KRUSE, R., Nefclass - a neuro-fuzzy approach for the classification of data, In: Proceedings of the Symposium on Applied Computing. 1995.

NAUCK, D., KRUSE, R. Neuro-fuzzy systems for function approximation. Fuzzy Sets and System. 101(2), pp. 261-271. 1999.

PAIVA, R. P., DOURADO, A. Interpretability and learning in neuro-fuzzy systems, Fuzzy Sets and System. 147, pp. 17-38. 2004.

SALA, A. Validación y Aproximación Funcional en Sistemas de Control Basados en Lógica Borrosa. Universidad Politécnica de Valencia. Tesis Doctoral. 1998.

SALA, A., ALBERTOS, P. Inference error minimisation: fuzzy modelling of ambiguous functions. Fuzzy Sets and Systems, 121 pp. 95 – 111. 2001.

SANTOS, M., LÓPEZ, R., de la CRUZ, J.M., Modelo predictivo neuro-borroso de la aceleración de cabeceo de un buque de alta velocidad. RIAI: Revista Iberoamericana de Automática e Informática Industrial. 2005.

SUGENO, M., YASUKAWA, T. A fuzzy logic based approach to qualitative modeling. Transactions on Fuzzy Systems, vol. 1, No. 1, pp. 7-31. 1993.

WANG, L-X, MENDEL, J.M. Generating fuzzy rules by learning form examples, IEEE Transactions on Systems Man and Cybernetics, vol. 22, no 6, pp. 1414-1427. 1992.

How to Cite
Gil Navia, F., & Contreras Montes, J. (2010). Fuzzy Predictive Model of the Vertical Acceleration of a High Speed Vessel in Pitch Motion. Ciencia Y tecnología De Buques, 3(6), 67-74. https://doi.org/10.25043/19098642.25
Scientific and Technological Research Articles