[1]FAN Yibo,ZHAO Tao,XIE Xiangpeng.Self-organizing rule generation method for a general type-2 fuzzy system[J].CAAI Transactions on Intelligent Systems,2024,19(3):646-652.[doi:10.11992/tis.202206024]
Copy
Self-organizing rule generation method for a general type-2 fuzzy system
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
19
Number of periods:
2024 3
Page number:
646-652
Column:
学术论文—智能系统
Public date:
2024-05-05
- Title:
- Self-organizing rule generation method for a general type-2 fuzzy system
- Keywords:
- self-organizing learning; fuzzy control; generalized type-2 fuzzy system; membership function; structure learning; error-driven method; incremental learning; recursive least-squares method
- CLC:
- TP273+.5
- DOI:
- 10.11992/tis.202206024
- Abstract:
- To solve the problem of difficult construction of appropriate fuzzy rules in the generalized type-2 fuzzy system due to a lack of expertise in complex situations, a method for generating generalized type-2 fuzzy sets and fuzzy rules based on the activation intensity of input data was proposed. The fuzzy rules of generalized type-2 fuzzy systems were constructed by a data-driven self-organization strategy, and the parameters of the front and rear parts of the system were optimized by the iterative least squares and gradient descent methods. Finally, the tracking simulation of the nonlinear system was conducted in the conditions of no disturbance and noise disturbance . The experimental results revealed that the generalized type-2 fuzzy system generated by the self-organizing rules is effective, and the reference trajectory can be tracked with high accuracy.
- References:
-
[1] MENDEL J M. Type-2 fuzzy sets and systems: an overview[J]. IEEE computational intelligence magazine, 2007, 2(1): 20–29.
[2] 钱殿伟. 基于模糊滑模的多机器人系统编队控制[J]. 智能系统学报, 2016, 11(5): 641–647
QIAN Dianwei. Formation control of multi-robot systems in a fuzzy sliding mode[J]. CAAI transactions on intelligent systems, 2016, 11(5): 641–647
[3] MA Zhiqiang, HUANG Panfeng, KUANG Zhian. Fuzzy approximate learning-based sliding mode control for deploying tethered space robot[J]. IEEE transactions on fuzzy systems, 2021, 29(9): 2739–2749.
[4] 秦贝贝, 陈增强, 孙明玮, 等. 基于自适应神经模糊推理系统的船舶航向自抗扰控制[J]. 智能系统学报, 2020, 15(2): 255–263
QIN Beibei, CHEN Zengqiang, SUN Mingwei, et al. Active disturbance rejection control of ship course based on adaptive-network-based fuzzy inference system[J]. CAAI transactions on intelligent systems, 2020, 15(2): 255–263
[5] KASABOV N K, SONG Qun. DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction[J]. IEEE transactions on fuzzy systems, 2002, 10(2): 144–154.
[6] SILVA A M, CAMINHAS W, LEMOS A, et al. A fast learning algorithm for evolving neo-fuzzy neuron[J]. Applied soft computing, 2014, 14: 194–209.
[7] SU W C, JUANG C F, HSU C M. Multiobjective evolutionary interpretable type-2 fuzzy systems with structure and parameter learning for hexapod robot control[J]. IEEE transactions on systems, man, and cybernetics:systems, 2022, 52(5): 3066–3078.
[8] LIANG Qilian, MENDEL J M. Interval type-2 fuzzy logic systems: theory and design[J]. IEEE transactions on fuzzy systems, 2000, 8(5): 535–550.
[9] MENDEL J M, JOHN R I, LIU Feilong. Interval type-2 fuzzy logic systems made simple[J]. IEEE transactions on fuzzy systems, 2006, 14(6): 808–821.
[10] MENDEL J M, LIU Xinwang. Simplified interval type-2 fuzzy logic systems[J]. IEEE transactions on fuzzy systems, 2013, 21(6): 1056–1069.
[11] ZHAO Tao, LIU Jiahao, DIAN Songyi. Finite-time control for interval type-2 fuzzy time-delay systems with norm-bounded uncertainties and limited communication capacity[J]. Information sciences:an international journal, 2019, 483(C): 153–173.
[12] ZHAO Tao, DIAN Songyi. State feedback control for interval type-2 fuzzy systems with time-varying delay and unreliable communication links[J]. IEEE transactions on fuzzy systems, 2018, 26(2): 951–966.
[13] LIN Yangyin, LIAO S H, CHANG J Y, et al. Simplified interval type-2 fuzzy neural networks[J]. IEEE transactions on neural networks and learning systems, 2014, 25(5): 959–969.
[14] HWANG C, RHEE F C H. Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means[J]. IEEE transactions on fuzzy systems, 2007, 15(1): 107–120.
[15] PUJIANTARA M, ABDILLAH M. Intelligent over current relays based on interval type 2 fuzzy logic approach[C]//2012 International Conference on Green and Ubiquitous Technology. Bandung: IEEE, 2012: 58-61.
[16] BIGLARBEGIAN M, MELEK W W, MENDEL J M. Design of novel interval type-2 fuzzy controllers for modular and reconfigurable robots: theory and experiments[J]. IEEE transactions on industrial electronics, 2011, 58(4): 1371–1384.
[17] CHEN C S. Supervisory adaptive tracking control of robot manipulators using interval type-2 TSK fuzzy logic system[J]. IET control theory & applications, 2011, 5(15): 1796–1807.
[18] LIU Feilong. An efficient centroid type-reduction strategy for general type-2 fuzzy logic system[J]. Information sciences:an international journal, 2008, 178(9): 2224–2236.
[19] MENDEL J M, Uncertain rule-based fuzzy logic system: introduction and new directions[M]. New York: Springer, 2001.
[20] KARNIK N N, MENDEL J M. Type-2 fuzzy logic systems: type-reduction[C]//SMC’98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics. San Diego: IEEE, 2002: 2046-2051.
[21] CASTILLO O, MELIN P. Optimization of type-2 fuzzy systems based on bio-inspired methods: a concise review[J]. Information sciences:an international journal, 2012, 205: 1–19.
[22] JUANG C F, TSAO Y W. A self-evolving interval type-2 fuzzy neural network with online structure and parameter learning[J]. IEEE transactions on fuzzy systems, 2008, 16(6): 1411–1424.
- Similar References:
Memo
-
Last Update:
1900-01-01