Información del artículo
Resumen
La lógica borrosa ha conseguido en un breve periodo de tiempo revolucionar la tecnología mediante la conjunción de los fundamentos matemáticos, la lógica y el razonamiento. Su inherente capacidad de hibridación y su robustez intrínseca han permitido a la lógica borrosa cosechar numerosos éxitos en el campo del modelado y el control de sistemas, impulsando el control inteligente. En este artículo se estudian los sistemas borrosos híbridos más usuales y su importancia en el campo del modelado y control de sistemas dinámicos. El trabajo presenta varios ejemplos que ilustran, para diferentes técnicas de hibridación, cómo éstas potencian las cualidades innatas de la lógica borrosa para el modelado y control de sistemas dinámicos. Así mismo, se incluyen más de ciento cincuenta referencias bibliográficas que permitirán al lector interesado profundizar en el campo de la lógica borrosa, y más concretamente en el de sus técnicas de hibridación con aplicación al modelado y control borroso.
Palabras clave:
Algoritmos bioinspirados
control borroso
control inteligente
modelado borroso
redes neuronales
sistemas borrosos
sistemas híbridos
Abstract
Fuzzy logic has revolutionized, in a short period of time, the technology through a combination of mathematical fundamentals, logic and reasoning. Its inherent hybridization ability and intrinsic robustness, have allowed to fuzzy logic get numerous successes in the field of modeling and control of systems, impulsing the intelligent control. In this paper, the more usual hybrid fuzzy systems and its importance in the field of modeling and control of dynamic systems are studied. The paper presents several examples that illustrate, for different hybridization techniques, how these enhance the innate qualities of fuzzy logic for modeling and control of dynamic systems. Also, more than a hundred and fifty references are included, which allow the interested reader to delve into the field of fuzzy logic, and more specifically, in its hybridization techniques with application to modeling and fuzzy control.
Keywords:
Bioinspired algorithms fuzzy control fuzzy modeling fuzzy systems hybrid systems intelligent control neuronal networks
Texto completo
Referencias no citadas
Al-Hadithi et al., 2013, Al-Hadithi et al., 2007, Albertos and Sala, 2004, Altrock, 1994, Altrock et al., 1992, Anderson et al., 1977, Andújar et al., 2006a, Andújar and Barragán, 2005, Andújar and Barragán, 2010, Andújar et al., 2006b, Andújar et al., 2009, Andújar et al., 2007, Andújar and Bravo, 2005, Andújar et al., 2004, Angelov and Buswell, 2002, Angelov et al., 2004, Angelov, 2002, Angelov and Filev, 2004, Aracil, 2000, Aracil et al., 1989, Aroba et al., 2007, Assilian, 1974, Barragán, 2009, Barragán et al., 2013, Baumann et al., 2000, Bellman and Zadeh, 1970, Berenji and Khedkar, 1992, Bezdek and Dunn, 1975, Bezdek et al., 1984, Bonissone et al., 1999, Bravo et al., 2002, Bulteau and Vial, 1983, Cao and Frank, 2000, Chen et al., 1995, Cherry and Jones, 1995, Chiu, 1994, Chua et al., 1987, Cordón et al., 2004, Cordón et al., 1995, Denaï et al., 2007, Dennis, 1977, Dennis and Schnabel, 1996, Dorigo et al., 1999, Dorigo et al., 1996, Driankov et al., 1993, Dunn, 1973, Feng et al., 2002, Ferrer et al., 1998, Freeman and Skapura, 1991, Gajate, 2010, Gajate and Haber, 2009, Goguen, 1969, Grande et al., 2005, Grewal and Andrews, 2001, Grippo et al., 1986, Grossberg, 1987, Gupta, 2000, Han and Shi, 2007, Haruki and Kikuchi, 1992, Hayashi and Takagi, 1988, Hofbauer et al., 1993, Hopfield, 1982, Horikawa et al., 1992, Jang, 1992, Jang, 1991, Jang, 1993, Jang and Sun, 1995, Jensen and Shen, 2005, Jiang and Li, 1996, Jiménez et al., 2009, Juang et al., 2008, Kalman, 1960, Karr, 1991, Karr et al., 1989, Kim et al., 1996, Kohonen, 1988, Kosko, 1994, Lako_, 1973, Layne et al., 1993, LeCun, 1985, Lee and Sul, 1998, Lee and Zak, 2002, Lennon and Passino, 1999, Li and Yonezawa, 1991, Liang and John, 1999, Lin and Wang, 2002, Lin and Lee, 1991, Lin and Wang, 1998, López-Baldán et al., 2002, Lughofer, 2008, Maeda and Murakami, 1991, Malki et al., 1994, Mamdani, 1974, Mamdani, 1977, Mamdani and Assilian, 1975, Mauer, 1995, Maybeck, 1979, McCulloch and Pitts, 1943, Meseguer and Sols, 1975, Mościński and Ogonowski, 1995, Mudi and Pal, 1999, Negoita, 1969, Negoita, 1974, Nguyen et al., 1995, Nijmeijer and Schaft, 1990, Nomura et al., 1993, Parker, 1985, Piegat, 1997, Piegat, 2001, Pok and Xu, 1994, Powell, 1984, Ramaswamy et al., 1993, Rao and Prahlad, 1997, Rosenblatt, 1958, Rosenblatt, 1962, Sala and Ariño, 2009, Santos, 2011, Santos et al., 1996, Schouten et al., 2002, Simon, 2002, Slotine and Li, 1991, Smith, 1970, Sols, 1975, Steinmüller and Wick, 1993, Sugeno, 1974, Sugeno and Yasukawa, 1993, Takagi, 1995, Takagi, 1990, Takagi and Hayashi, 1991, Tanaka et al., 1998, Tanaka and Sugeno, 1992, Tanaka and Wang, 2001, Tang et al., 2001, Vachtsevanos et al., 1993, Vélez et al., 2010, Wang and Tanaka, 1996, Wang and Langari, 1994, Wang, 1992, Wang, 1994, Wang, 1997, Wang and Mendel, 1992, Werbos, 1974, Widrow, 1959, Widrow and Ho, 1989, Won and Langari, 2002, Xie and Beni, 1991, Yen and Langari, 1999, Ying, 1998, Ying et al., 1990, Yongman et al., 1994, Zadeh, 1962, Zadeh, 1965, Zadeh, 1973, Zeng, 2000, Zhao et al., 1993 and Zimmermann, 1976.
Referencias
[Al-Hadithi et al., 2013]
Al-Hadithi, B.M., Barragán, A.J., Andújar, J.M., Jiménez, A., Oct. 2013. Variable structure control with chattering elimination and guaranteed stability for a generalized t-s model. Applied Soft Computing 13 (12), 4802-4812. DOI: http://dx.doi.org/10.1016/j.asoc.2013.07.026.
[Al-Hadithi et al., 2007]
Al-Hadithi, B.M., Matía, F., Jiménez, A., Apr. 2007. Análisis de estabilidad de sistemas borrosos. Revista Iberoamericana de Automática e Informática Industrial (RIAI) 4 (2), 7-25.
[Albertos and Sala, 2004]
Albertos, P., Sala, A., Jul. 2004. Control borroso. Una metodología integradora. Revista Iberoamericana de Automática e Informática Industrial (RIAI) 1 (2), 22-31.
[Altrock, 1994]
Altrock, C. v., 1994. Fuzzy logic technologies in automotive engineering. En: Embedded systems conference. Vol. 2. pp. 407-422.
[Altrock et al., 1992]
Altrock, C. v., Krause, B., Zimmermann, H.J., 1992. Advanced fuzzy logic control technologies in automotive applications. En: IEEE Conference on Fuzzy Systems. pp. 831-842.
[Anderson et al., 1977]
Anderson, J.A., Silverstein, J.W., Ritz, S.A., Jones, R.S., Sep. 1977. Distinctive features, categorical perception, and probability learning: some applications of a neural model. Psychological Review 84 (5), 413-451. DOI: 10.1037/0033-295X.84.5.413.
[Andújar et al., 2006a]
Andújar, J.M., Aroba, J., Torre, M.L. d. l., Grande, J.A., Jan. 2006a. Contrast of evolution models for agricultural contaminants in ground waters by means of fuzzy logic and data mining. Environmental Geology 49 (3), 458-466. DOI: 10.1007/s00254-005-0103-2.
[Andújar and Barragán, 2005]
Andújar, J.M., Barragán, A.J., Sep. 2005. A methodology to design stable nonlinear fuzzy control systems. Fuzzy Sets and Systems 154 (2), 157-181. DOI: 10.1016/j.fss.2005.03.006.
[Andújar and Barragán, 2010]
Andújar, J.M., Barragán, A.J., Jul. 2010. A formal methodology for the analysis and design of nonlinear fuzzy control systems. En: Fuzzy Systems (FUZZ), 2010 IEEE International Conference on. No. 1. Barcelona, Spain, pp. 66-74. DOI: 10.1109/FUZZY. 2010.5583980.
[Andújar et al., 2006b]
Andújar, J.M., Barragán, A.J., Córdoba, J.M., Fernández, I., Jan. 2006b. Diseño de sistemas de control borroso: modelado de la planta. Revista Iberoamericana de Automática e Informática Industrial (RIAI) 3 (1), 75-81.
[Andújar et al., 2009]
Andújar, J.M., Barragán, A.J., Gegúndez, M.E., Oct. 2009. A general and formal methodology for designing stable nonlinear fuzzy control systems. IEEE Transactions on Fuzzy Systems 17 (5), 1081-1091. DOI: 10.1109/TFUZZ. 2009.2021984.
[Andújar et al., 2007]
Andújar, J.M., Barragán, A.J., Gegúndez, M.E., Maestre, M., 2007. Control borroso multivariable basado en heurística. un caso práctico: Grúa porta contenedortes. Revista Iberoamericana de Automática e Informática Industrial (RIAI) 4 (2), 81-89+123.
[Andújar and Bravo, 2005]
Andújar, J.M., Bravo, J.M., Mar. 2005. Multivariable fuzzy control applied to the physical-chemical treatment facility of a cellulose factory. Fuzzy Sets and Systems 150 (3), 475-492. DOI: 10.1016/j.fss.2004.03.023.
[Andújar et al., 2004]
Andújar, J.M., Bravo, J.M., Peregrín, A., Dec. 2004. Stability analysis and synthesis of multivariable fuzzy systems using interval arithmetic. Fuzzy Sets and Systems 148 (3), 337-353. DOI: 10.1016 issn = 0165-0114,/j.fss.2004.01.008.
[Angelov and Buswell, 2002]
Angelov, P., Buswell, R., Oct. 2002. Identification of evolving fuzzy rule-based models. IEEE Transactions on Fuzzy Systems 10 (5), 667-677. DOI: 10.1109/TFUZZ. 2002.803499.
[Angelov et al., 2004]
Angelov, P., Xydeas, C., Filev, D., july 2004. On-line identification of MIMO evolving Takagi–Sugeno fuzzy models. En: Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on. Vol. 1. pp. 55-60. DOI: 10.1109/FUZZY. 2004.1375687.
[Angelov, 2002]
Angelov, P.P., 2002. Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems. Vol. 92 of Studies in Fuzziness and Soft Computing. Physica-Verlag, Springer, New York.
[Angelov and Filev, 2004]
Angelov, P.P., Filev, D.P., Feb. 2004. An approach to online identification of Takagi–Sugeno fuzzy models. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics 34 (1), 484-498. DOI: 10.1109/TSMCB. 2003.817053.
[Aracil, 2000]
Aracil, J., 2000. Stability Issues in Fuzzy Control. Springer-Verlag New York, Inc., Secaucus, NJ, USA.
[Aracil et al., 1989]
Aracil, J., Ollero, A., Garcia-Cerezo, A., Sep. 1989. Stability indices for the global analysis of expert control systems. IEEE Transactions on Systems, Man, and Cybernetics 19 (5), 998-1007. DOI: 10.1109/21.44014.
[Aroba et al., 2007]
Aroba, J., Grande, J.A., Andújar, J.M., De La Torre, M.L., Riquelme, J.C., Sep. 2007. Application of fuzzy logic and data mining techniques as tools for qualitative interpretation of acid mine drainage processes. Environmental. Geology 53 (1), 135-145. DOI: 10.1007/s00254-006-0627-0.
[Assilian, 1974]
Assilian, S., 1974. Artificial intelligence in the control of real dynamical systems. Ph.D. thesis, Queen Mary College, London University. Babuška, R., Mar. 1995. Fuzzy modeling – a control engineering perspecti ve. En: Proceedings of FUZZ-IEEE/IFES’95. Vol. 4. Yokohama, Japan, pp. 1897-1902. DOI: 10.1109/FUZZY. 1995.409939.
[Barragán, 2009]
Barragán, A.J., 2009. Síntesis de sistemas de control borroso estables por diseño. Universidad de Huelva. URL: http://uhu.es/antonio.barragan/thesis.
[Barragán et al., 2013]
Barragán, A.J., Al-Hadithi, B.M., Jiménez, A., Andújar, J.M., 2013. A general methodology for online TS fuzzy modeling by the extended kalman filter. Applied Soft Computing, in–press. DOI: 10.1016/j.asoc.2013.09.005.
[Baumann et al., 2000]
Baumann, B., Washington, G., Glenn, B., Rizzoni, G., Mar. 2000. Mechatronic design and control of hybrid electric vehicles. IEEE/ASME Transactions on Mechatronics 5 (1), 58-72. DOI: 10.1109/3516.828590.
[Bellman and Zadeh, 1970]
Bellman, R.E., Zadeh, L.A., Dec. 1970. Decision–making in a fuzzy environment. Management Science 17 (4), B141-B164.
[Berenji and Khedkar, 1992]
Berenji, H.R., Khedkar, P., Sep. 1992. Learning and tuning fuzzy logic controllers through reinforcements. IEEE Transactions on Neural Networks 3 (5), 724-740. DOI: 10.1109/72.159061.
[Bezdek and Dunn, 1975]
Bezdek, J.C., Dunn, J.C., Aug. 1975. Optimal fuzzy partitions: A heuristic for estimating the parameters in a mixture of normal distributions. IEEE Transactions on Computers C-24 (8), 835-838.
[Bezdek et al., 1984]
Bezdek, J.C., Ehrlich, R., Full, W.E., 1984. FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences 10 (2-3), 191-203. DOI: 10.1016/0098-3004(84)90020-7.
[Bonissone et al., 1999]
Bonissone, P.P., Chen, Y.H., Goebel, K.F., Khedkar, P.S., 1999. Hybrid soft computing systems: Industrial and commercial applications. Proceedings of the IEEE 87 (9), 1641-1667. DOI: 10.1109/5.784245.
[Bravo et al., 2002]
Bravo, J.M., Sánchez, O., Andújar, J.M., Fernández, E., 2002. Stability analysis and synthesis of fuzzy systems using interval arithmetic. En: 2002, E.S.L., (Ed.), 15th IFAC World Congress. No. 15. Barcelona, Spain, pp. 79-79. DOI: 10.1016/j.fss.2004.01.008.
[Bulteau and Vial, 1983]
Bulteau, J., Vial, J., Feb. 1983. Curvilinear path and trust region in unconstrained optimization: a convergence analysis. Mathematical Programming Study 1, 82-101.
[Cao and Frank, 2000]
Cao, Y.Y., Frank, P.M., Apr. 2000. Analysis and synthesis of nonlinear timedelay systems via fuzzy control approach. IEEE Transactions on Fuzzy Systems 8 (2), 200-211. DOI: 10.1109/91.842153.
[Chen et al., 1995]
Chen, O.T., Lu, Y.-C., Chang, H.-T., 1995. Fuzzy reasoning processor for camera image autofocus. En: Wu, L.T., (Ed.), Visual Communications and Image Processing’95. Vol. 2501. SPIE, pp. 347-354. DOI: 10.1117/12.206740.
[Cherry and Jones, 1995]
Cherry, A., Jones, R., Mar. 1995. Fuzzy logic control of an automotive suspension system. En: IEE Proceedings: Control Theory and Applications. IEE, Stevenage, United Kingdom, pp. 149-160. DOI: 10.1049/ip-cta:19951736.
[Chiu, 1994]
Chiu, S., 1994. Fuzzy model identification based on cluster estimation. En: Journal of Intelligent & Fuzzy Systems. Vol. 2. pp. 267-278.
[Chua et al., 1987]
Chua, L.O., Desoer, C.A., Kuh, E.S., 1987. Linear and nonlinear circuits. McGraw-Hill series in electrical and computer engineering: Circuits and systems. McGraw-Hill Book Company, New York.
[Cordón et al., 2004]
Cordón, O., Gomide, F., Herrera, F., Ho_mann, F., Magdalena, L., Jan. 2004. Ten years of genetic fuzzy systems: Current framework and new trends. Fuzzy Sets and Systems 141 (1), 5-31. DOI: 10.1016/S0165-0114(03)00111-8.
[Cordón et al., 1995]
Cordón, O., Herrera, F., Lozano, M., 1995. A classified review on the combination fuzzy logic-genetic algorithms bibliography. Tech. Rep. DECSAI-95129, Dept. of Computer Science and A.I., University of Granada.
[Denaï et al., 2007]
Denaï, M.A., Palis, F., Zeghbib, A.H., Jun. 2007. Modeling and control of nonlinear systems using soft computing techniques. Applied Soft Computing 7 (3), 728-738. DOI: 10.1016/j.asoc.2005.12.005.
[Dennis, 1977]
Dennis Jr., J., 1977. Nonlinear least-squares. State of the art in numerical analysis. Academic Press, pp. 269-312.
[Dennis and Schnabel, 1996]
Dennis Jr., J., Schnabel, R.B., 1996. Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Vol. 16 of Classics in Applied Mathematics. Soc for Industrial & Applied Math.
[Dorigo et al., 1999]
Dorigo, M., Di Caro, G., Gambardella, L.M., 1999. Ant algorithms for discrete optimization. Artificial Life 5 (2), 137-172. DOI: 10.1162/106454699568728.
[Dorigo et al., 1996]
Dorigo, M., Maniezzo, V., Colorni, A., Feb. 1996. Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics 26 (1), 29-41. DOI: 10.1109/3477.484436.
[Driankov et al., 1993]
Driankov, D., Hellendoorn, H., Reinfrank, M., 1993. An introduction to fuzzy control. Springer-Verlag, New York, USA.
[Dunn, 1973]
Dunn, J.C., 1973. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3, 32-57.
[Feng et al., 2002]
Feng, G., Cao, S.G., Rees, N.W., Oct. 2002. Stable adaptive control of fuzzy dynamic systems. Fuzzy Sets and Systems 131 (2), 217-224. DOI: 10.1016/S0165-0114(01)00236-6.
[Ferrer et al., 1998]
Ferrer, J., Rodrigo, M.A., Seco, A., Penya-roja, J.M., 1998. Energy saving in the aeration process by fuzzy logic control. Water Science and Technology 38 (3), 209-217. DOI: 10.1016/S0273-1223(98)00463-6.
[Freeman and Skapura, 1991]
Freeman, J.A., Skapura, D.M., 1991. Neural networks: algorithms, applications, and programming techniques. Addison Wesley Longman Publishing Co., Inc., Redwood City, CA, USA.
[Gajate, 2010]
Gajate, A., 2010. Modelado y control neuroborroso de sistemas complejos. Aplicación a procesos de mecanizado de alto rendimiento. Ph.D. thesis, Dpto. de Informática Industrial, Centro de Automática y Robótica (CSICUPM).
[Gajate and Haber, 2009]
Gajate, A., Haber, R.E., Jan. 2009. Networked neurofuzzy control. An application to a drilling process. RIAI - Revista Iberoamericana de Automatica e Informatica Industrial 6 (1), 31-38+127.
[Goguen, 1969]
Goguen, J.A., Apr. 1969. The logic of inexact concepts. Synthese 19 (3-4), 325-373. DOI: 10.1007/BF00485654.
[Grande et al., 2005]
Grande, J.A., Andújar, J.M., Aroba, J., De La Torre, M.L., Beltrán, R., Apr. 2005. Precipitation, pH and metal load in AMD river basins: An application of fuzzy clustering algorithms to the process characterization. Journal of Environmental Monitoring 7 (4), 325-334. DOI: 10.1039/b410795k.
[Grewal and Andrews, 2001]
Grewal, M.S., Andrews, A.P., 2001. Kalman Filtering: Theory and Practice Using MATLAB, 2nd Edición. John Wiley & Sons, Inc., Hoboken, New Yersey.
[Grippo et al., 1986]
Grippo, L., Lampariello, F., Lucidi, S., 1986. A nonmonotone line search technique for newton's method. SIAM Journal on Numerical Analysis 23 (4), 707-716. DOI: 10.1137/0723046.
[Grossberg, 1987]
Grossberg, S., 1987. Competitive learning: from interactive activation to adaptive resonance. Cognitive Science 11 (1), 23-63. DOI: 10.1016/S0364-0213(87)80025-3.
[Gupta, 2000]
Gupta, M., 2000. Soft Computing and Intelligent Systems. Theory and Applications. Academic Press, Hamilton, Ontario, Canada.
[Han and Shi, 2007]
Han, Y.F., Shi, P., Jan. 2007. An improved ant colony algorithm for fuzzy clustering in image segmentation. Neurocomputing 70 (4-6), 665-671. DOI: 10.1016/j.neucom.2006.10.022.
[Haruki and Kikuchi, 1992]
Haruki, T., Kikuchi, K., Aug. 1992. Video camera system using fuzzy logic. IEEE Transactions on Consumer Electronics 38 (3), 624-634. DOI: 10.1109/30.156746.
[Hayashi and Takagi, 1988]
Hayashi, I., Takagi, H., May 1988. Formulation of fuzzy reasoning by neural network (in japanese). En: IFSA Japan. pp. 55-60.
[Hofbauer et al., 1993]
Hofbauer, P., Arend, H.O., Pfannstiel, D., 1993. New heating systems controls based on the use of fuzzy logic. En: First European Congress on Fuzzy and Intelligent Technologies. Aachen, pp. 1036-1042.
[Hopfield, 1982]
Hopfield, J.J., Apr. 1982. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America 79 (8), 2554-2558.
[Horikawa et al., 1992]
Horikawa, S.-I., Furuhashi, T., Uchikawa, Y., Sep. 1992. On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm. IEEE Transactions on Neural Networks 3 (5), 801-806. DOI: 10.1109/72.159069.
[Jang, 1992]
Jang, J.-S., 1992. Neuro-fuzzy modeling: architectures, analyses, and applications. Ph.D. thesis, University of California, Berkeley.
[Jang, 1991]
Jang, J.-S. R., 1991. Fuzzy modeling using generalized neural networks and kalman filter algorithm. AAAI’91, Proceedings of the ninth National conference on Artificial intelligence 91, 762-767.
[Jang, 1993]
Jang, J.-S. R., May 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23 (3), 665-685. DOI: 10.1109/21.256541.
[Jang and Sun, 1995]
Jang, J.-S. R., Sun, C.-T., Mar. 1995. Neuro-fuzzy modeling and control. Proceedings of the IEEE 83 (3), 378-406. DOI: 10.1109/5.364486.
[Jensen and Shen, 2005]
Jensen, R.P., Shen, Q., Jan. 2005. Fuzzy-rough data reduction with ant colony optimization. Fuzzy Sets and Systems 149 (1), 5-20. DOI: 10.1016/j.fss.2004.07.014.
[Jiang and Li, 1996]
Jiang, T., Li, Y.T., Aug. 1996. Generalized defuzzification strategies and their parameter learning procedures. IEEE Transactions on Fuzzy Systems 4 (1), 64-71. DOI: 10.1109/91.481845.
[Jiménez et al., 2009]
Jiménez, A., Aroba, J., de la Torre, M.L. d. l., Andújar, J.M., Grande, J.A., 2009. Model of behaviour of conductivity versus pH in acid mine drainage water, based on fuzzy logic and data mining techniques. Journal of Hydroinformatics 2 (11), 147-153. DOI: 10.2166/hydro.2009.015.
[Juang et al., 2008]
Juang, C.F., Lu, C.M., Lo, C., Wang, C.Y., Mar. 2008. Ant colony optimization algorithm for fuzzy controller design and its fpga implementation. IEEE Transactions on Industrial Electronics 55 (3), 1453-1462. DOI: 10.1109/TIE. 2007.909762.
[Kalman, 1960]
Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. Transactions on ASME-Journal of Basic Engineering 82 (series D), 35-45.
[Karr, 1991]
Karr, C.L., Jul. 1991. Design of an adaptive fuzzy logic controller using a genetic algorithm. En: 4th International Conference on Genetic Algorithms, ICGA’91. Morgan Kaufmann, San Diego, CA, USA, pp. 450-457.
[Karr et al., 1989]
Karr, C.L., Freeman, L.M., Meredith, D.L., Nov. 1989. Improved fuzzy process control of spacecraft autonomous rendezvous using a genetic algorithm. En: SPIE Conf. on Intelligent Control and Adaptive Systems. Vol. 1196. pp. 274-283. URL: http://dx.doi.org/10.1117/12.969926 DOI: 10.1117/12.969926.
[Kim et al., 1996]
Kim, H.M., Dickerson, J., Kosko, B., Dec. 1996. Fuzzy throttle and brake control for platoons of smart cars. Fuzzy Sets and Systems 84 (3), 209-234. DOI: 10.1016/issn = 0165-0114,0165-0114(95)00326-6.
[Kohonen, 1988]
Kohonen, T., 1988. Self Organization and Associative Memory. Springer Series in Information Sciences. Springer London, Limited.
[Kosko, 1994]
Kosko, B., Nov. 1994. Fuzzy systems as universal approximators. IEEE Transactions on Computers 43 (11), 1329-1333. DOI: 10.1109/12.324566.
[Lako_, 1973]
Lako_, G., Oct. 1973. Hedges: A study in meaning criteria and the logic of fuzzy concepts. Journal of Philosophical Logic 2 (4), 458-508. DOI: 10.1007/BF00262952.
[Layne et al., 1993]
Layne, J.R., Passino, K.M., Yurkovich, S., Jun. 1993. Fuzzy learning control for antiskid braking systems. IEEE Transactions on Control Systems Technology 1 (2), 122-129. DOI: 10.1109/87.238405.
[LeCun, 1985]
LeCun, Y., 1985. Une procedure d’apprentissage pour reseau a seuil symetrique (in french). Cognitiva. CESTA, Paris, France 85, 599-604.
[Lee and Sul, 1998]
Lee, H.-D., Sul, S.-K., Aug. 1998. Fuzzy-logic-based torque control strategy for parallel-type hybrid electric vehicle. IEEE Transactions on Industrial Electronics 45 (4), 625-632. DOI: 10.1109/41.704891.
[Lee and Zak, 2002]
Lee, Y., Zak, S., Apr. 2002. Designing a genetic neural fuzzy antilock-brakesystem controller. IEEE Transactions on Evolutionary Computation 6 (2), 198-211. DOI: 10.1109/4235.996019.
[Lennon and Passino, 1999]
Lennon,W. K., Passino, K.M., Mar. 1999. Intelligent control for brake systems. IEEE Transactions on Control Systems Technology 7 (2), 188-202. DOI: 10.1109/87.748145.
[Li and Yonezawa, 1991]
Li, Y., Yonezawa, Y., 1991. Stability analysis of fuzzy control systems by the hyperstability theorem. Japanese journal of fuzzy theory and systems 3 (2), 209-214.
[Liang and John, 1999]
Liang, W., John, Y., Feb. 1999. Extracting fuzzy rules for system modeling using a hybrid of genetic algorithms and Kalman filter. Fuzzy Sets and Systems 101 (3), 353-362. DOI: 10.1016/S0165-0114(97)00098-5.
[Lin and Wang, 2002]
Lin, C.-F.,Wang, S.-D., Mar. 2002. Fuzzy support vector machines. IEEE Transactions on Neural Networks 13 (2), 464-471. DOI: 10.1109/72.991432.
[Lin and Lee, 1991]
Lin, C.-T., Lee, C., Dec. 1991. Neural-network-based fuzzy logic control and decision system. IEEE Transactions on Computers 40 (12), 1320-1336. DOI: 10.1109/12.106218.
[Lin and Wang, 1998]
Lin, H.-R., Wang, W.-J., Nov. 1998. L2-stabilization design for fuzzy control systems. Fuzzy Sets and Systems 100 (1-3), 159-172. DOI: 10.1016/S0165-0114(97)00131-0.
[López-Baldán et al., 2002]
López-Baldán, M.J., García-Cerezo, A., Cejudo, J.M., Romero, A., Apr. 2002. Fuzzy modeling of a thermal solar plant. International Journal of Intelligent Systems 17 (4), 369-379. DOI: 10.1002/int.10026.
[Lughofer, 2008]
Lughofer, E.D., Dec. 2008. FLEXFIS: A robust incremental learning approach for evolving Takagi-Sugeno fuzzy models. IEEE Transactions on Fuzzy Systems 16 (6), 1393-1410. DOI: 10.1109/TFUZZ. 2008.925908.
[Maeda and Murakami, 1991]
Maeda, M., Murakami, S., 1991. Stability analysis of fuzzy control systems using phase planes. En: Japanese journal of fuzzy theory and systems. Vol. 3. pp. 149-160.
[Malki et al., 1994]
Malki, H.A., Li, H., Chen, G., Nov. 1994. New design and stability analysis of fuzzy proportional-derivative control systems. IEEE Transactions on Fuzzy Systems 2 (4), 245-254. DOI: 10.1109/91.324804.
[Mamdani, 1974]
Mamdani, E.H., Dec. 1974. Application of fuzzy algorithms for control of a simple dynamic plant. Proceedings of the IEEE 121 (12), 1585-1588. DOI: 10.1049/piee.1974.0328.
[Mamdani, 1977]
Mamdani, E.H., Dec. 1977. Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions on Computers C-26 (12), 1182-1191. DOI: 10.1109/TC. 1977.1674779.
[Mamdani and Assilian, 1975]
Mamdani, E.H., Assilian, S., 1975. An experimental in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7 (1), 1-13.
[Mauer, 1995]
Mauer, G.F., Nov. 1995. Fuzzy logic controller for an ABS braking system. IEEE Transactions on Fuzzy Systems 3 (4), 381-388. DOI: 10.1109/91.481947.
[Maybeck, 1979]
Maybeck, P.S., 1979. Stochastic models, estimation, and control. Vol. 141 of Mathematics in Science and Engineering. Academyc Press, 11 Fifth Avenue, New York.
[McCulloch and Pitts, 1943]
McCulloch, W.S., Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics 5 (4), 115-133. DOI: 10.1007/BF02478259.
[Meseguer and Sols, 1975]
Meseguer, J., Sols, I., 1975. Automata in semimodule categories. En: Proceedings of the First International Symposium on Category Theory Applied to Computation and Control. Springer-Verlag, London, UK, pp. 193-198.
[Mościński and Ogonowski, 1995]
Mościński, J., Ogonowski, Z., 1995. Advanced control with MATLAB and SIMULINK. Ellis Horwood, Upper Saddle River, NJ, USA.
[Mudi and Pal, 1999]
Mudi, R., Pal, N., Feb. 1999. A robust self-tuning scheme for PI- and PD-type fuzzy controllers. IEEE Transactions on Fuzzy Systems 7 (1), 2-16. DOI: 10.1109/91.746295.
[Negoita, 1969]
Negoita, C.V., 1969. Information retrieval systems. Ph.D. thesis, Polytechnical Institute, Bucharest, Romania.
[Negoita, 1974]
Negoita, C.V., 1974. Fuzzy systems and artificial intelligence. Cybernetes 3, 173-178.
[Nguyen et al., 1995]
Nguyen, H.T., Sugeno, M., Tong, R.M., Yager, R.R., 1995. Theoretical aspects of fuzzy control. John Wiley Sons, New York, NY, USA.
[Nijmeijer and Schaft, 1990]
Nijmeijer, H., Schaft, A. v. d., 1990. Nonlinear dynamical control systems. Springer Verlag, Berlin.
[Nomura et al., 1993]
Nomura, H., Hayashi, I., Wakami, N., 1993. A self–tuning method of fuzzy inference rules by descent method. En: Lowen, R., Roubens, M., (Eds.), Fuzzy Logic. Kluwer Academic Publishers, Dordrecht, pp. 465-475.
[Parker, 1985]
Parker, D., 1985. Learning logic. Tech. Rep. TR-47, Center for Computational Res. Econ. Manage. Sci. Massachusetts Inst. Technol., Cambridge, MA.
[Piegat, 1997]
Piegat, A., 1997. Hyperstability of fuzzy-control systems and degrees of freedom. En: 5th European Congress on Intelligent Techniques and Soft Computing, EUFIT’97. Vol. 2. pp. 1446-1450.
[Piegat, 2001]
Piegat, A., 2001. Fuzzy modeling and control. Springer-Verlag Company, Heidelberg-New York.
[Pok and Xu, 1994]
Pok, Y.-M., Xu, J.-X., Jun. 1994. Why is fuzzy control robust? En: Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on. Vol. 2. Orlando, FL, pp. 1018-1022. DOI: 10.1109/FUZZY. 1994.343875.
[Powell, 1984]
Powell, M., Aug. 1984. On the global convergence of trust region algorithms for unconstrained minimization. Mathematical Programming 29 (3), 297-303. DOI: 10.1007/BF02591998.
[Ramaswamy et al., 1993]
Ramaswamy, P., Riese, M., Edwards, R.M., Lee, K.Y., Dec. 1993. Two approaches for automating the tuning process of fuzzy logic controllers. En: 32nd IEEE Conference on Decision and Control. Part 2 (of 4). San Antonio, TX, USA. DOI: 10.1109/CDC. 1993.325490.
[Rao and Prahlad, 1997]
Rao, M.V.C. adn Prahlad, V., Jan. 1997. A tunable fuzzy logic controller for vehicle-active suspension systems. Fuzzy Sets and Systems 85 (1), 11-21. DOI: 10.1016/0165-0114(95)00369-X.
[Rosenblatt, 1958]
Rosenblatt, F., 1958. The perceptron: a probabilistic model for information storage and organization in the brain. Psycological Review 65 (6), 386-408.
[Rosenblatt, 1962]
Rosenblatt, F., 1962. Principles of neurodynamics: perceptrons and the theory of brain mechanisms. Report (Cornell Aeronautical Laboratory). Spartan Books.
[Sala and Ariño, 2009]
Sala, A., Ariño, C., Apr. 2009. Reduciendo distancias entre el control borroso y el control no lineal: luces y sombras. Revista Iberoamericana de Automática e Informática Industrial (RIAI) 6 (2), 26-35.
[Santos, 2011]
Santos, M., Oct. 2011. Un enfoque aplicado del control inteligente. RIAI - Revista Iberoamericana de Automatica e Informatica Industrial 8 (4), 283-296. DOI: 10.1016/j.riai.2011.09.016.
[Santos et al., 1996]
Santos, M., Dormido, S., de la Cruz, J.M., 1996. Fuzzy-PID controllers vs. fuzzy-PI controllers. En: 5th IEEE International Conference on Fuzzy Systems FUZZ-IEEE’96. pp. 1598-1604. DOI: 10.1109/FUZZY. 1996.552571.
[Schouten et al., 2002]
Schouten, N., Salman, M., Kheir, N., May 2002. Fuzzy logic control for parallel hybrid vehicles. IEEE Transactions on Control Systems Technology 10 (3), 460-468. DOI: 10.1109/87.998036.
[Simon, 2002]
Simon, D., 2002. Training fuzzy systems with the extended Kalman filter. Fuzzy Sets and Systems 132 (2), 189-199. DOI: 10.1016/S0165-0114(01)00241-X.
[Slotine and Li, 1991]
Slotine, J.-J. E., Li, W., 1991. Applied nonlinear control. Prentice-Hall, NJ.
[Smith, 1970]
Smith, R.E., 1970. Measure theory on fuzzy sets. Ph.D. thesis, Dept. of Mathematics, University of Saskatchewan, Saskatoon, Canada.
[Sols, 1975]
Sols, I., 1975. Aportaciones a la teoría de topos, al álgebra universal y a las matemáticas fuzzy. Ph.D. thesis, Universidad de Zaragoza, Zaragoza, España.
[Steinmüller and Wick, 1993]
Steinmüller, H., Wick, O., 1993. Fuzzy and neurofuzzy applications in european washing machines. En: First European Congress on Fuzzy and Intelligent Technologies, EUFIT’93. Aachen, pp. 1031-1035.
[Sugeno, 1974]
Sugeno, M., 1974. Theory of fuzzy integrals and its applications. Ph.D. thesis, Tokyo Institute of Technology.
[Sugeno and Yasukawa, 1993]
Sugeno, M., Yasukawa, T., Feb. 1993. Fuzzy-logic-based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems 1 (1), 7-31. DOI: 10.1109/TFUZZ. 1993.390281.
[Takagi, 1995]
Takagi, H., 1995. Industrial Applications of Fuzzy Control and Intelligent Systems. IEEE Press, Piscataway, NJ, USA.
[Takagi, 1990]
Takagi, H., Apr. 1996. Industrial and commercial applications of nn/fs/ga/chaos in 1990s. En: International Workshop on Soft Computing in Industry (IWSCI’ 96). Muroran, Hokkaido, Japan, pp. 160-165.
[Takagi and Hayashi, 1991]
Takagi, H., Hayashi, I., May 1991. NN-driven fuzzy reasoning. International Journal of Approximate Reasoning 5 (3), 191-212.
[Tanaka et al., 1998]
Tanaka, K., Ikeda, T., Wang, H.O., May 1998. Fuzzy regulators and fuzzy observers: relaxed stability conditions and LMI–based designs. IEEE Transactions on Fuzzy Systems 6 (2), 250-265. DOI: 10.1109/91.669023.
[Tanaka and Sugeno, 1992]
Tanaka, K., Sugeno, M., Jan. 1992. Stability analysis and design of fuzzy control systems. Fuzzy Sets and Systems 45 (2), 135-156. DOI: 10.1016/0165-0114(92)90113-I.
[Tanaka and Wang, 2001]
Tanaka, K., Wang, H.O., 2001. Fuzzy control systems design and analysis: a linear matrix inequality approach. AWiley-Interscience publication. Wiley.
[Tang et al., 2001]
Tang, K., Man, K.F., Chen, G., Kwong, S., Aug. 2001. An optimal fuzzy PID controller. IEEE Transactions on Industrial Electronics 48 (4), 757-765. DOI: 10.1109/41.937407.
[Vachtsevanos et al., 1993]
Vachtsevanos, G., Farinwata, S., Pirovolou, D., Jun. 1993. Fuzzy logic control of an automotive engine. IEEE Control Systems Magazine 13 (3), 62-68. DOI: 10.1109/37.214946.
[Vélez et al., 2010]
Vélez, M.A., Sánchez, O., Romero, S., Manuel, A.J., Mar. 2010. A new methodology to improve interpretability in neuro-fuzzy TSK models. Applied Soft Computing 10 (2), 578-591. DOI: http://dx.doi.org/10.1016/j.asoc.2009.08.027.
[Wang and Tanaka, 1996]
Wang, H.O., Tanaka, K., Gri_n, M.F., Feb. 1996. An approach to fuzzy control of nonlinear systems: stability and design issues. IEEE Transactions on Fuzzy Systems 4 (1), 14-23. DOI: 10.1109/91.481841.
[Wang and Langari, 1994]
Wang, L., Langari, R., 1994. Fuzzy controller design via hyperstability approach. En: IEEE International Conference on Fuzzy Systems. Vol. 1. pp. 178-182. DOI: 10.1109/FUZZY. 1994.343696.
[Wang, 1992]
Wang, L.-X., 1992. Fuzzy systems are universal approximators. En: IEEE International Conference on Fuzzy Systems. San Diego, CA, USA, pp. 1163-1170. DOI: 10.1109/FUZZY. 1992.258721.
[Wang, 1994]
Wang, L.X., 1994. Adaptive fuzzy systems and control. Prentice Hall, New Jersey.
[Wang, 1997]
Wang, L.-X., 1997. A course in fuzzy systems and control. Prentice Hall, New Yersey, USA.
[Wang and Mendel, 1992]
Wang, L.-X., Mendel, J.M., Mar. 1992. Back-propagation fuzzy system as nonlinear dynamic system identifiers. En: Fuzzy Systems, 1992, IEEE International Conference on. pp. 1409-1418. DOI: 10.1109/FUZZY. 1992.258711.
[Werbos, 1974]
Werbos, P., 1974. Beyond regresion: new tools for prediction and analysis in the behavioral sciences. Ph.D. thesis, Harvard University, Cambridge, MA.
[Widrow, 1959]
Widrow, B., 1959. Adaptative sampled-data systems, a statistical theory of adaptation. En: IRE WESCON Convention Record. Part 4. Institute of Radio Engineers, New York.
[Widrow and Ho, 1989]
Widrow, B., Ho_, M.E., 1989. Adaptive switching circuits. En: Wescon Conference Record. pp. 709-717.
[Won and Langari, 2002]
Won, J.-S., Langari, R., Feb. 2002. Fuzzy torque distribution control for a parallel hybrid vehicle. Expert Systems 19 (1), 4-10. DOI: 0.1111/1468-0394.00184.
[Xie and Beni, 1991]
Xie, X.L., Beni, G., Aug. 1991. A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 13 (8), 841-847. DOI: 10.1109/34.85677.
[Yen and Langari, 1999]
Yen, J., Langari, R., 1999. Fuzzy Logic: Intelligence, control, and information. Prentice Hall.
[Ying, 1998]
Ying, H., Jul. 1998. Su_cient conditions on uniform approximation of multivariate functions by general Takagi–Sugeno fuzzy systems with linear rule consequent. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 28 (4), 515-520. DOI: 10.1109/3468.686713.
[Ying et al., 1990]
Ying, H., Siler,W., Buckley, J.J., May 1990. Fuzzy control theory: A nonlinear case. Automatica 26 (3), 513-520. DOI: 10.1016/0005-1098(90)90022-A.
[Yongman et al., 1994]
Yongman, L., Seong-Ik, J., Keewook, C., Dongyun, L., Wonchan, K., Choong-Woong, L., May 1994. Fuzzy-control processor for automatic focusing. IEEE Transactions on Consumer Electronics 40 (2), 138-144. DOI: 10.1109/30.286408.
[Zadeh, 1962]
Zadeh, L.A., May 1962. From circuit theory to system theory. En: Proceedings of th Institute of Radio Engineers. Vol. 50. pp. 856-865. DOI: 10.1109/JRPROC. 1962.288302.
[Zadeh, 1965]
Zadeh, L.A., 1965. Fuzzy sets. Information and Control 8 (3), 338-353.
[Zadeh, 1973]
Zadeh, L.A., Jan. 1973. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics 3, 28-44. DOI: 10.1109/TSMC. 1973.5408575.
[Zeng, 2000]
Zeng, K., Zhang, N.-Y., Xu, W.-L., Dec. 2000. A comparative study on sufficient conditions for Takagi–Sugeno fuzzy systems as universal approximators. IEEE Transactions on Fuzzy Systems 8 (6), 773-780. DOI: 10.1109/91.890337.
[Zhao et al., 1993]
Zhao, Z.-Y., Tomizuka, M., Isaka, S., Sep. 1993. Fuzzy gain scheduling of PID controllers. IEEE Transactions on Systems, Man, and Cybernetics 23 (5), 1392-1398. DOI: 10.1109/21.260670.
[Zimmermann, 1976]
Zimmermann, H.J., 1976. Description and optimization of fuzzy systems. International Journal of General Systems 2, 209-215.