Citation: Yang Chen, Jinxia Wu, Jie Lan. Study on reasonable initialization enhanced Karnik-Mendel algorithms for centroid type-reduction of interval type-2 fuzzy logic systems[J]. AIMS Mathematics, 2020, 5(6): 6149-6168. doi: 10.3934/math.2020395
[1] | D. R. Wu, J. M. Mendel, Uncertainty measures for interval type-2 fuzzy sets, Inf. Sci., 177 (2007), 5378-5393. doi: 10.1016/j.ins.2007.07.012 |
[2] | J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Englewood Cliffs, NJ, USA: Prentice-Hall, 2001, 1-547. |
[3] | P. Melin, L. Astudillo, O. Castillo, et al. Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm, Expert Syst. Appl., 40 (2013), 3185-3195. doi: 10.1016/j.eswa.2012.12.032 |
[4] | H. Hagras, A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots, IEEE Trans. Fuzzy Syst., 12 (2004), 524-539. doi: 10.1109/TFUZZ.2004.832538 |
[5] | C. W. Tao, J. S. Taur, C. W. Chang, et al. Simplified type-2 fuzzy sliding controller for wing rocket system, Fuzzy Sets Syst., 207 (2012), 111-129. doi: 10.1016/j.fss.2012.02.015 |
[6] | D. Bernardo, H. Hagras, E. Tsang, A genetic type-2 fuzzy logic based system for the generation of summarized linguistic predictive models for financial applications, Soft Comput., 17 (2013), 2185-2201. |
[7] | Y. Chen, D. Z. Wang, S. C. Tong, Forecasting studies by designing Mamdani interval type-2 fuzzy logic systems: With combination of BP algorithms and KM algorithms, Neurocomputing, 174 (2016), 1133-1146. |
[8] | A. Khosravi, S. Nahavandi, D. Creighton, et al., Interval type-2 fuzzy logic systems for load forecasting: a comparative study, IEEE Trans. Power Syst., 27 (2012), 1274-1282. doi: 10.1109/TPWRS.2011.2181981 |
[9] | S. Barkat, A. Tlemcani, H. Nouri, Noninteracting adaptive control of PMSM using interval type-2 fuzzy logic systems, IEEE Trans. Fuzzy Syst., 19 (2011), 925-936. doi: 10.1109/TFUZZ.2011.2152815 |
[10] | D. Z. Wang, Y. Chen, Study on permanent magnetic drive forecasting by designing Takagi Sugeno Kang type interval type-2 fuzzy logic systems, Trans. Institute Meas. Control, 40 (2018), 2011-2023. |
[11] | Y. Chen, D. Z. Wang, Forecasting by designing Mamdani general type-2 fuzzy logic systems optimized with quantum particle swarm optimization algorithms, Trans. Institute Meas. Control, 41 (2019), 2886-2896. |
[12] | P. Melin, O. Mendoza, O. Castillo, An improved method for edge detection based on interval type-2 fuzzy logic, Expert Syst. Appl., 37 (2010), 8527-8535. |
[13] | C. S. Lee, M. H. Wang, H. Hagras, Type-2 fuzzy ontology and its application to personal diabetic-diet recommendation, IEEE Trans. Fuzzy Syst., 18 (2010), 316-328. |
[14] | G. M. Méndez, M. D. L. A. Hernandez, Hybrid learning for interval type-2 fuzzy logic systems based on orthogonal least-squares and back-propagation methods, Inf. Sci., 179 (2009), 2146-2157. |
[15] | G. M. Méndez, M. D. L. A. Hernandez, Hybrid learning mechanism for interval A2-C1 type-2 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems, Inf. Sci., 220 (2013), 149-169. doi: 10.1016/j.ins.2012.01.024 |
[16] | T. Wang, Y. Chen, S. C. Tong, Fuzzy reasoning models and algorithms on type-2 fuzzy sets, Int. J. Innovative Comput. Inf. Control, 4 (2008), 2451-2460. |
[17] | J. M. Mendel, General type-2 fuzzy logic systems made simple: A tutorial, IEEE Trans. Fuzzy Sys., 22 (2014), 1162-1182. |
[18] | J. M. Mendel, On KM algorithms for solving type-2 fuzzy set problems, IEEE Trans. Fuzzy Syst., 21 (2013), 426-446. doi: 10.1109/TFUZZ.2012.2227488 |
[19] | D. R. Wu, J. M. Mendel, Enhanced Karnik-Mendel algorithms, IEEE Trans. Fuzzy Syst., 17 (2009), 923-934. doi: 10.1109/TFUZZ.2008.924329 |
[20] | J. M. Mendel, F. L. Liu, Super-exponential convergence of the Karnik-Mendel algorithms for computing the centroid of an interval type-2 fuzzy set, IEEE Trans. Fuzzy Syst., 15 (2007), 309-320. doi: 10.1109/TFUZZ.2006.882463 |
[21] | X. W. Liu, J. M. Mendel, D. R. Wu, Study on enhanced Karnik-Mendel algorithms: Initialization explanations and computation improvements, Inf. Sci., 184 (2012), 75-91. doi: 10.1016/j.ins.2011.07.042 |
[22] | J. W. Li, R. John, S. Coupland, et al., On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets, IEEE Trans. Fuzzy Syst., 26 (2018), 1036-1039. |
[23] | Y. Chen, Study on weighted Nagar-Bardini algorithms for centroid type-reduction of interval type-2 fuzzy logic systems, J. Intell. Fuzzy Syst., 34 (2018), 2417-2428. |
[24] | J. M. Mendel, R. I. John, F. L. Liu, Interval type-2 fuzzy logic systems made simple, IEEE Trans. Fuzzy Syst., 14 (2006), 808-821. doi: 10.1109/TFUZZ.2006.879986 |
[25] | Y. Chen, D. Z. Wang, Study on centroid type-reduction of general type-2 fuzzy logic systems with weighted Nie-Tan algorithms, Soft Comput., 22 (2018), 7659-7678. |
[26] | F. L. Liu, An efficient centroid type-reduction strategy for general type-2 fuzzy logic system, Inf. Sci., 178 (2008), 2224-2236. doi: 10.1016/j.ins.2007.11.014 |
[27] | J. M. Mendel, X. W. Liu, Simplified interval type-2 fuzzy logic systems, IEEE Trans. Fuzzy Syst., 21 (2013), 1056-1069. |
[28] | S. Greenfield, F. Chiclana, Accuracy and complexity evaluation of defuzzification strategies for the discretised interval type-2 fuzzy set, Int. J. Approximate Reasoning, 54 (2013), 1013-1033. |
[29] | Y. Chen, Study on centroid type-reduction of interval type-2 fuzzy logic systems based on noniterative algorithms, Complexity, 2019 (2019), 1-12. |
[30] | T. Kumbasar, Revisiting Karnik-Mendel algorithms in the framework of linear fractional programming, Int. J. Approximate Reasoning, 82 (2017), 1-21. |
[31] | S. Greenfield, F. Chiclana, S. Coupland, et al., The collapsing method of defuzzification for discretised interval type-2 fuzzy sets, Inf. Sci., 179 (2009), 2055-2069. doi: 10.1016/j.ins.2008.07.011 |
[32] | D. R. Wu, Approaches for reducing the computational cost of interval type-2 fuzzy logic systems: overview and comparisons, IEEE Trans. Fuzzy Syst., 21 (2013), 80-99. |
[33] | M. A. Khanesar, A. Jalalian, O. Kaynak, Improving the speed of center of set type-reduction in interval type-2 fuzzy systems by eliminating the need for sorting, IEEE Trans. Fuzzy Syst., 25 (2017), 1193-1206. doi: 10.1109/TFUZZ.2016.2602392 |
[34] | Y. Chen, D. Z. Wang, W. Ning, Forecasting by TSK general type-2 fuzzy logic systems optimized with genetic algorithms, Optimal Control Appl. Methods, 39 (2018), 393-409. |
[35] | Y. Chen, D. Z. Wang, Forecasting by general type-2 fuzzy logic systems optimized with QPSO algorithms, Int. J. Control, Automation Syst., 15 (2017), 2950-2958. |
[36] | F. Gaxiola, P. Melin, F. Valdez, et al. Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO, Appl. Soft Comput., 38 (2016), 860-871. doi: 10.1016/j.asoc.2015.10.027 |
[37] | Q. F. Fan, T. Wang, Y. Chen, et al., Design and application of interval type-2 TSK fuzzy logic system based on QPSO algorithm, Int. J. Fuzzy Syst., 20 (2018), 835-846. doi: 10.1007/s40815-017-0357-3 |
[38] | C. H. Hsu, C. F. Juang, Evolutionary robot wall-following control using type- 2 fuzzy controller with species-de-activated continuous ACO, IEEE Trans. Fuzzy Syst., 21 (2013), 100-112. |
[39] | D. R. Wu, J. M. Mendel, Recommendations on designing practical interval type-2 fuzzy systems, Eng. Appl. Artif. Intell., 85 (2019), 182-193. |
[40] | X. L. Liu, S. P. Wan, Combinatorial iterative algorithms for computing the centroid of an interval type-2 fuzzy set, IEEE Trans. Fuzzy Syst., 2019, DOI: 10.1109/TFUZZ.2019.2911918. |
[41] | H. Z. Hu, Y. Wang, Y. L. Cai, Advantages of the enhanced opposite direction searching algorithm for computing the centroid of an interval type-2 fuzzy set, Asian J. Control, 14 (2012), 1422-1430. |
[42] | J. H. Hu, P. P. Chen, Y. Yang, The fruit fly optimization algorithms for patient-centered care based on interval trapezoidal type-2 fuzzy numbers, Int. J. Fuzzy Syst., 21 (2019), 1270-1287. |
[43] | M. Javanmard, H. Mishmast Nehi, A solving method for fuzzy linear programming problem with interval type-2 fuzzy numbers, Int. J. Fuzzy Syst., 21 (2019), 882-891. |
[44] | C. Chen, R. John, J. Twycross, et al. A direct approach for determining the switch points in the Karnik-Mendel algorithm, IEEE Trans. Fuzzy Syst., 26 (2018), 1079-1085. doi: 10.1109/TFUZZ.2017.2699168 |
[45] | O. Castillo, L. Amador-Angulo, J. R. Castro, et al. A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems, Inf. Sci., 354 (2016), 257-274. |
[46] | L. Cervantes, O. Castillo, Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control, Inf. Sci., 324 (2015), 247-256. |
[47] | O. Castillo, P. Melin, E. Ontiveros, et al. A high-speed interval type 2 fuzzy system approach for dynamic parameter adaptation in metaheuristics, Eng. Appl. Artificial Intelligence, 85 (2019), 666-680. |
[48] | E. Ontiveros-Robles, P. Melin, O. Castillo, Comparative analysis of noise robustness of type 2 fuzzy logic controllers, Kybernetika, 54 (2018), 175-201. |
[49] | E. Ontiveros-Robles, P. Melin, O. Castillo, New methodology to approximate type-reduction based on a continuous root-finding karnik mendel algorithm, Algorithms, 10 (2017), 77-96. doi: 10.3390/a10030077 |
[50] | Y. Chen, Study on sampling-based discrete noniterative algorithms for centroid type-reduction of interval type-2 fuzzy logic systems, Soft Comput., 24 (2020), 11819-11828. |
[51] | S. C. Tong, Y. M. Li, Robust adaptive fuzzy backstepping output feedback tracking control for nonlinear system with dynamic uncertainties, Sci. China Inf. Sci., 53 (2010), 307-324. doi: 10.1007/s11432-010-0031-y |
[52] | S. C. Tong, Y. M. Li, Observer-based adaptive fuzzy backstepping control of uncertain pure-feedback systems, Sci. China Inf. Sci., 57 (2014), 1-14. |
[53] | M. Deveci, I. Z. Akyurt, S. Yavuz, GIS-based interval type-2 fuzzy set for public bread factory site selection, J. Enterprise Inf. Manage., 31 (2018), 820-847. |