[en] This paper reports the use of simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of interval and general type-2 fuzzy logic systems to maximize their modeling ability. The combination of simulated annealing with these models is presented in the modeling of four benchmark problems including real-world problems. The type-2 fuzzy logic system models are compared in their ability to model uncertainties associated with these problems. Issues related to this combination between simulated annealing and fuzzy logic systems, including type-2 fuzzy logic systems, are discussed. The results demonstrate that learning the third dimension in type-2 fuzzy sets with a deterministic defuzzifier can add more capability to modeling than interval type-2 fuzzy logic systems. This finding can be seen as an important advance in type-2 fuzzy logic systems research and should increase the level of interest in the modeling applications of general type-2 fuzzy logic systems, despite their greater computational load.
Disciplines :
Computer science
Author, co-author :
Almaraashi, Majid
John, Robert
Hopgood, Adrian ; Université de Liège > HEC - Ecole de gestion de l'ULG : Direction générale
Ahmadi, Samad
Language :
English
Title :
Learning of interval and general type-2 fuzzy logic systems using simulated annealing: Theory and practice
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Bibliography
E. Aarts, and J. Lenstra Local Search in Combinatorial Optimization 2003 Princeton University Press
E.H.L. Aarts, and H.M.M.T. Eikelder Simulated annealing P. Pardalos, M. Resende, Handbook of Applied Optimization 2002 Oxford University Press 209 220
M. Almaraashi, and R. John Tuning of type-2 fuzzy systems by simulated annealing to predict time series Proceedings of the World Congress on Engineering 2011, WCE 2011 vol. 2 2011 Newswood Limited London, UK 976 980
M. Almaraashi, and R. John Tuning type-2 fuzzy systems by simulated annealing to estimate maintenance cost Proceedings of the UK Workshop on Computational Intelligence, UKCI 2011 2011 Manchester
M. Almaraashi, R. John, and S. Ahmadi Electrical engineering and intelligent systems book S.I. Ao, L. Gelman, Learning of Type-2 Fuzzy Logic Systems by Simulated Annealing with Adaptive Step Size Lecture Notes in Electrical Engineering vol. 130 2012 Springer
M. Almaraashi, R. John, and S. Coupland Designing generalised type-2 fuzzy logic systems using interval type-2 fuzzy logic systems and simulated annealing Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ) 2012 IEEE
M. Almaraashi, R. John, and A. Hopgood Automatic learning of general type-2 fuzzy logic systems using simulated annealing Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2014 IEEE 2384 2390 10.1109/FUZZ-IEEE.2014.6891694
J. Branke, S. Meisel, and C. Schmidt Simulated annealing in the presence of noise J. Heuristics 14 6 2008 627 654
G. Casillas, Fuzzy Modeling Library (fmlib), 2011, (Available at http://decsai.ugr.es/~casillas/fmlib/index.html). (accessed 11.03.28).
H.H. Christian Wagner Novel methods for the design of general type-2 fuzzy sets based on device characteristics and linguistic labels surveys Proceedings of 2009 IFSA World Congress and EUSFLAT World Conference 2009 Lisbon, Portugal 537 543
O. Cordón, F. Herrera, and L. Sánchez Solving electrical distribution problems using hybrid evolutionary data analysis techniques Appl. Intell. 10 1 1999 5 24
O. Cordón, F. Herrera, and P. Villar Analysis and guidelines to obtain a good uniform fuzzy partition granularity for fuzzy rule-based systems using simulated annealing Int. J. Approx. Reason. 25 3 2000 187 215
O. Cordón, F. Herrera, and P. Villar Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base IEEE Trans. Fuzzy Syst. 9 4 2001 667 674
O. Cordón, F. Herrera, and I. Zwir Linguistic modeling by hierarchical systems of linguistic rules IEEE Trans. Fuzzy Syst. 10 1 2002 2 20
S. Coupland, and R. John Geometric type-1 and type-2 fuzzy logic systems IEEE Trans. Fuzzy Syst. 15 1 2007 3 15 10.1109/TFUZZ.2006.889764
L. Drack, and H. Zadeh Soft computing in engineering design optimisation J. Intell. Fuzzy Syst. 17 4 2006 353 365
C. Gafa, and S. Coupland A new recursive type-reduction procedure for general type-2 fuzzy sets Proceedings of 2011 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems, T2FUZZ 2011 44 49 10.1109/T2FUZZ.2011.5949548
S. Greenfield, F. Chiclana, S. Coupland, and R. John The collapsing method of defuzzification for discretised interval type-2 fuzzy sets Inf. Sci. 179 13 2009 2055 2069
S. Greenfield, R. John, and S. Coupland A novel sampling method for type-2 defuzzification Proceedings of the UK Workshop on Computational Intelligence, UKCI 2005 2005 London 120 127
F. Guely, R. La, and P. Siarry Fuzzy rule base learning through simulated annealing Fuzzy Sets Syst. 105 3 1999 353 363
H. Hagras Type-2 flcs: A new generation of fuzzy controllers IEEE Comput. Intell. Mag. 2 1 2007 30 43
H. Hamrawi, S. Coupland, and R. John A novel alpha-cut representation for type-2 fuzzy sets Proceedings of International Conference on Fuzzy Systems, FUZZ IEEE 2010 and World Congress on Computational Intelligence, WCCI 2010 2010 IEEE Barcelona, Spain 1 8
L. Ingber Simulated annealing: practice versus theory Math. Comput. Model. 18 11 1993 29 57
W. Jeng, C. Yeh, and S. Lee General type-2 fuzzy neural network with hybrid learning for function approximation Proceedings of International Conference on Fuzzy Systems, FUZZ IEEE, 2009 2009 IEEE 1534 1539
R. John Perception Modelling Using Type-2 Fuzzy Sets (Ph.D. thesis) R.I. John, 2000 De Montfort University
R. John, and S. Coupland Extensions to type-1 fuzzy: type-2 fuzzy logic and uncertainty Comput. Intell.: Princ. Pract. 2006 89 102
R. John, and S. Coupland Type-2 fuzzy logic: a historical view IEEE Comput. Intell. Mag. 2 2007 57 62
R. John, and C. Czarnecki A type 2 adaptive fuzzy inferencing system Proceedings of 1998 IEEE International Conference on Systems, Man, and Cybernetics vol. 2 1998 IEEE 2068 2073
N. Karnik, and J. Mendel Operations on type-2 fuzzy sets Fuzzy Sets Syst. 122 2 2001 327 348
S. Kirkpatrick, C. Gelatt, and M. Vecchi Optimization by simulated annealing, 1983 Science 220 1983 671 680
O. Linda, and M. Manic Importance sampling based defuzzification for general type-2 fuzzy sets Proceedings of 2010 IEEE International Conference on Fuzzy Systems, FUZZ 2010 1 7 10.1109/FUZZY.2010.5584256
F. Liu An efficient centroid type-reduction strategy for general type-2 fuzzy logic system Inf. Sci. 178 9 2008 2224 2236
L. Lucas, T. Centeno, and M. Delgado General type-2 fuzzy inference systems: analysis, design and computational aspects Proceedings of IEEE International Fuzzy Systems Conference, FUZZ-IEEE 2007 2007 1 6
M. Mackey, and L. Glass Oscillation and chaos in physiological control systems Science 197 4300 1977 287 289
P. Melin, C. Gonzalez, J. Castro, O. Mendoza, and O. Castillo Edge-detection method for image processing based on generalized type-2 fuzzy logic IEEE Trans. Fuzzy Syst. 22 6 2014 1515 1525 10.1109/TFUZZ.2013.2297159
J. Mendel Uncertain Rule-based Fuzzy Logic Systems: Introduction and New Directions 2001 Prentice Hall
J. Mendel Fuzzy sets for words: a new beginning Proceedings of the 12th IEEE International Conference on Fuzzy Systems, FUZZ'03 vol. 1 2003
J. Mendel, and R. John Type-2 fuzzy sets made simple IEEE Trans. Fuzzy Syst. 10 2 2002 117 127 10.1109/91.995115
J. Mendel, R. John, and F. Liu Interval type-2 fuzzy logic systems made simple IEEE Trans. Fuzzy Syst. 14 6 2006 808 821 10.1109/TFUZZ.2006.879986
J. Mendel, and F. Liu On new quasi-type-2 fuzzy logic systems Proceedings of IEEE World Congress on Computational Intelligence and IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2008 2008 IEEE 354 360
J. Mendel, F. Liu, and D. Zhai Alpha plane representation for type-2 fuzzy sets: theory and applications IEEE Trans. Fuzzy Syst. 17 5 2009 1189 1207 10.1109/TFUZZ.2009.2024411
P. Salamon, P. Sibani, and R. Frost Facts, conjectures, and improvements for simulated annealing 2002 Society for Industrial Mathematics
J.T. Starczewski Efficient triangular type-2 fuzzy logic systems Int. J. Approx. Reason. 50 5 2009 799 811
C. Wagner, and H. Hagras zslices based general type-2 FLC for the control of autonomous mobile robots in real world environments Proceedings of IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2009 2009 718 725 10.1109/FUZZY.2009.5277383
C. Wagner, and H. Hagras Toward general type-2 fuzzy logic systems based on z-slices IEEE Trans. Fuzzy Syst. 18 4 2010 637 660 10.1109/TFUZZ.2010.2045386
C. Wagner, and H. Hagras Uncertainty and type-2 fuzzy sets and systems Proceedings of 2010 UK Workshop on Computational Intelligence, UKCI 2010 1 5 10.1109/UKCI.2010.5625603
C. Wagner, S. Miller, J. Garibaldi, D. Anderson, and T. Havens From interval-valued data to general type-2 fuzzy sets IEEE Trans. Fuzzy Syst. 23 2 2015 248 269 10.1109/TFUZZ.2014.2310734
S. White Concepts of scale in simulated annealing Am. Inst. Phys. Conf. Ser. vol. 122 1984 261 270
T. Yanar, and Z. Akyrek Fuzzy model tuning using simulated annealing Expert Syst. Appl. 38 7 2011 8159 8169
L. Zadeh The concept of a linguistic variable and its application to approximate reasoning. I Inf. Sci. 8 1975 199 249
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