Wang F Y. Modeling, analysis and synthesis of linguistic dynamic systems: a computational theory. In: Proceedings of IEEE International Workshop on Architecture for Semiotic Modeling and Situation Control in Large Complex System. Monterey, CA: IEEE, 1995. 173-178
[2]
Wang F Y. Outline of a computing theory for linguistic dynamical systems: towards computing with words. International Journal of Intelligent Control and Systems, 1998, 2(2): 211-224
[3]
Mo Hong, Wang Fei-Yue. Linguistic dynamic systems based on computing with words and their stabilities. Science in China Series F: Information Sciences, 2009, 52(5): 780-796 (in Chinese)
[4]
Zadeh L A. Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems, 1996, 4(2): 103-111
[5]
Zadeh L A. From computing with numbers to computing with words —— from manipulation of measurements to manipulation of perceptions. IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, 1999, 45(1): 105-119
[6]
Rodríguez R M, Martinez L. An analysis of symbolic linguistic computing models in decision making. International Journal of General Systems, 2013, 42(1): 121-136
[7]
Wu D R. A reconstruction decoder for computing with words. Information Sciences, 2014, 255: 1-15
[8]
Mendel J M, Wu D R. Challenges for perceptual computer applications and how they were overcome. IEEE Computational Intelligence Magazine, 2012, 7(3): 36-47
[9]
Wu D R, Mendel J M. Aggregation using the linguistic weighted average and interval type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 2007, 15(6): 1145-1161
[10]
Liu F L, Mendel J M. Aggregation using the fuzzy weighted average as computed by the Karnik-Mendel algorithms. IEEE Transactions on Fuzzy Systems, 2008, 16(1): 1-12
[11]
Mendel J M, Wu D R. Perceptual reasoning for perceptual computing. IEEE Transactions on Fuzzy Systems, 2008, 16(6): 1550-1564
[12]
Mo Hong, Wang Fei-Yue, Zhao Liang. LDS trajectories under one-to-one mappings in interval type-2 fuzzy sets. Pattern Recognition and Artificial Intelligence, 2010, 23(2): 144-147 (in Chinese)
[13]
Zhao Liang. Research on the Interval Type-2 Fuzzy Method Based Computing with Words and Linguistic Dynamic Systems [Ph.D. dissertation]. Institute of Automation, Chinese Academy of Sciences, China, 2009 (in Chinese)
[14]
Zhao L. The class-2 linguistic dynamic trajectories of the interval type-2 fuzzy sets. In: Proceedings of the 2010 International Conference on Life System Modeling and Intelligent Computing. Wuxi, China: Springer, 2010. 342-349
[15]
Ban A I, Coroianu L. Nearest interval, triangular and trapezoidal approximation of a fuzzy number preserving ambiguity. International Journal of Approximate Reasoning, 2012, 53(5): 805-836
[16]
Mendel J M, Wu H W. Type-2 fuzzistics for symmetric interval type-2 fuzzy sets: part 1, forward problems. IEEE Transactions on Fuzzy Systems, 2006, 14(6): 781-792
[17]
Mendel J M, Wu H W. Type-2 fuzzistics for nonsymmetric interval type-2 fuzzy sets: forward problems. IEEE Transactions on Fuzzy Systems, 2007, 15(5): 916-930
[18]
Liu F L, Mendel J M. Encoding words into interval type-2 fuzzy sets using an interval approach. IEEE Transactions on Fuzzy Systems, 2008, 16(6): 1503-1521
[19]
Coupland S, Mendel J M, Wu D R. Enhanced interval approach for encoding words into interval type-2 fuzzy sets and convergence of the word fous. In: Proceedings of 2010 IEEE International Conference on Fuzzy Systems. Barcelona, Spain: IEEE, 2010. 1-8
[20]
Jia X Y, Liao W H, Tang Z M, Shang L. Minimum cost attribute reduction in decision-theoretic rough set models. Information Sciences, 2013, 219: 151-167
[21]
Grzymala-Busse J W, Marepally S R, Yao Y Y. An empirical comparison of rule sets induced by LERS and probabilistic rough classification. In: Rough Sets and Intelligent Systems-Professor Zdzislaw Pawlak in Memoriam. Springer: Berlin Heidelberg, 2013. 261-276
[22]
Juang C F, Huang R B, Cheng W Y. An interval type-2 fuzzy-neural network with support-vector regression for noisy regression problems. IEEE Transactions on Fuzzy Systems, 2010, 18(4): 686-699
[23]
Wang Fei-Yue. Fundamental issues in research of computing with words and linguistic dynamic systems. Acta Automatica Sinica, 2005, 31(6): 844-852 (in Chinese)
[24]
Wang F Y. On the abstraction of conventional dynamic systems: from numerical analysis to linguistic analysis. Information Science, 2005, 171(1-3): 233-259
[25]
Wang F Y, Tao Y. On linguistic analysis of numerical dynamic systems. In: Proceedings of the 2002 IEEE International Symposium on Intelligent Control. Vancouver, BC, Canada: IEEE, 2002. 850-855
[26]
Mo Hong, Wang Fei-Yue, Xiao Zhi-Quan, Chen Qian. Stabilities of linguistic dynamic systems based on interval type-2 fuzzy sets. Acta Automatica Sinica, 2011, 37(8): 1018-1024 (in Chinese)
[27]
Mo Hong. Linguistic dynamic orbits in the time varying universe of discourse. Acta Automatica Sinica, 2012, 38(10): 1585-1594 (in Chinese)
[28]
Rajati M R, Mendel J M. Novel weighted averages versus normalized sums in computing with words. Information Sciences, 2013, 235: 130-149
[29]
Bilgin A, Hagras H, Malibari A, Alhaddad M J, Alghazzawi D. Towards a linear general type-2 fuzzy logic based approach for computing with words. Soft Computing, 2013, 17(12): 2203-2222
[30]
Mendel J M, Zadeh L, Trillas E, Yager R, Lawry J, Hagras H, Guadarrama S. What computing with words means to me. IEEE Computational Intelligence Magazine, 2010, 5(1): 20-26
[31]
Franco C, Rodríguez J T, Montero J. An ordinal approach to computing with words and the preference caversion model. Information Sciences, 2014, 258: 239-248
[32]
Yang X J, Yan L L, Peng H, Gao X D. Encoding words into cloud models from interval-valued data via fuzzy statistics and membership function fitting. Knowledge-Based Systems, 2014, 55: 114-124
[33]
Wang J H, Hao J. An approach to computing with words based on canonical characteristic values of linguistic labels. IEEE Transactions on Fuzzy Systems, 2007, 15(4): 593-604
[34]
Mendel J M. Computing with words, when words can mean different things to different people. In: Proceedings of 3rd International ICSC Symposium on Fuzzy Logic and Applications. Rochester, NY: Rochester University, 1999. 158-164
[35]
Türk?en I B. Type 2 representation and reasoning for CWW. Fuzzy Sets and Systems, 2002, 127(1): 17-36
[36]
Mendel J M. The perceptual computer: an architecture for computing with words. In: Proceedings of the 2001 IEEE International Conference on Fuzzy Systems. Melbourne Australia: IEEE, 2001. 35-38
[37]
Mendel J M. An architecture for making judgments using computing with words. International Journal of Applied Mathematics and Computer Science, 2002, 12(3): 325-335
[38]
Mendel J M, Wu D R. Perceptual reasoning: a new computing with words engine. In: Proceedings of the 3rd IEEE International Conference on Granular Computing. California, USA: IEEE, 2007. 446-451
[39]
Wu D R, Mendel J M. Perceptual reasoning using interval type-2 fuzzy sets: properties. In: Proceedings of the 2008 IEEE International Conference on Fuzzy Systems. Hong Kong, China: IEEE, 2008. 1219-1226
[40]
Wu D R, Mendel J M. Perceptual reasoning for perceptual computing: a similarity-based approach. IEEE Transactions on Fuzzy Systems, 2009, 17(6): 1397-1411
[41]
Mo Hong, Wang Tao. Computing with words in generalized interval type-2 fuzzy sets. Acta Automatica Sinica, 2012, 38(5): 707-715 (in Chinese)
[42]
Zadeh L A. Fuzzy sets. Information and Control, 1965, 8(3): 338-353
[43]
Mendel J M. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Upper-Saddle River, NJ: Prentice-Hall, 2001.
[44]
Wei S H, Chen S M. Fuzzy risk analysis based on interval-valued fuzzy numbers. Expert Systems with Applications, 2009, 36(2): 2285-2299
[45]
Mendel J M, Wu H. Type-2 fuzzistics for symmetric interval type-2 fuzzy sets: part 2, inverse problems. IEEE Transactions on Fuzzy Systems, 2007, 15(2): 301-308.
[46]
Li C D, Zhang G Q, Yi J Q, Wang M. Uncertainty degree and modeling of interval type-2 fuzzy sets: definition, method and application. Computers and Mathematics with Applications, 2013, 66(10): 1822-1835
[47]
Wu D R, Mendel J M, Coupland S. Enhanced interval approach for encoding words into interval type-2 fuzzy sets and its convergence analysis. IEEE Transactions on Fuzzy Systems, 2012, 20(3): 499-513
[48]
Li C D, Zhang G Q, Wang M, Yi J Q. Data-driven modeling and optimization of thermal comfort and energy consumption using type-2 fuzzy method. Soft Computing, 2013, 17(11): 2075-2088
[49]
Pawlak Z, Roman S. Rough set approach to multi-attribute decision analysis. European Journal of Operational Research, 1994, 72(3): 443-459
[50]
Qian Y H, Liang J Y, Pedrycz W, Dang C Y. Positive approximation: an accelerator for attribute reduction in rough set theory. Artificial Intelligence, 2010, 174(9-10): 597-618
[51]
Grzymala-Busse J W. A new version of the rule induction system LERS. Fundamenta Informaticae, 1997, 31(1): 27-39
[52]
Huang C C, Tseng T L, Fan Y N, Hsu C H. Alternative rule induction methods based on incremental object using rough set theory. Applied Soft Computing, 2013, 13(1): 372-389