A self-evolving interval type-2 fuzzy neural network for nonlinear systems identification

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 17, Issue 1 PART 1, 2008, Pages

  Juang, Chia Feng ^a   Lu, Chun Feng ^a   Tsao, Yu Wei ^a  

^a NONE

Author keywords

Nonlinear system identification

Indexed keywords

GRADIENT DESCENT ALGORITHMS; INPUT SPACE; INTERVAL TYPE-2 FUZZY SETS; KALMAN FILTER ALGORITHMS; LEARNING PERFORMANCE; NONLINEAR SYSTEM IDENTIFICATION; NONLINEAR SYSTEMS IDENTIFICATION; ON-LINE STRUCTURES; ONLINE-CLUSTERING; PARAMETER LEARNING; TAKAGI-SUGENO; TYPE-2 FUZZY SYSTEMS;

CONTROL THEORY; FUZZY NEURAL NETWORKS; FUZZY RULES; FUZZY SETS; LEARNING ALGORITHMS; NONLINEAR SYSTEMS;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 79961019923     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20080706-5-KR-1001.0444     Document Type: Conference Paper

References (14)

1. Hagras, H (2004). "A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots", IEEE Trans. Fuzzy Systems, vol.12, no.524-539.
2. Hagras, H (2006). "Comments on dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)," IEEE Trans. Syst., Man and Cyber. - Part B: Cybernetics, vol.36, no.5, pp. 1206-1209.
3. Jang, J. S (1993). "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Trans. Syst., Man, Cybern., vol.23, no.3, pp. 665-685.
4. Juang, C. F and C. T. Lin (1998). "An on-line self-constructing neural fuzzy inference network and its applications," IEEE Trans. Fuzzy Systems, vol.6. no.1, pp. 12-32. (Pubitemid 128747936)
5. Karnik, N. N, J. M. Mendel, and Q. Liang (1999). "Type-2 fuzzy logic systems," IEEE Trans. on Fuzzy Systems, vol.7, no.6, pp. 643-658.
6. Kukolj, D and E. Levi (2004). "Identification of complex systems based on neural and Takagi-Sugeno fuzzy model," IEEE Trans. Sys., Man, Cybern., Part B: Cybernetics, vol.34, no.1, pp. 272-282.
7. Lee, C. H, Y. C. Lin, and W. Y. Lai (2003). "Systems identification using type-2 fuzzy neural network (Type-2 FNN) systems," Proc. IEEE Int. Symp. Computational Intelligence in Robotics and Automation, vol.3, pp. 1264-1269.
8. Liang, Q and J. M. Mendel (2000). "Interval type-2 fuzzy logic systems: theory and design," IEEE Trans. On Fuzzy Systems, vol.8, no.5, pp. 535-550. (Pubitemid 30966979)
9. Lin, C. J. Lin and C. T. Lin (1997). "An ART-based fuzzy adaptive learning control network," IEEE Trans. Fuzzy Systems, vol.5, no.4, pp. 477-496. (Pubitemid 127768529)
10. Melin, P and O. Castillo (2004). "Intelligent control of non-linear dynamic plants using type-2 fuzzy logic and neural networks," Proc. IEEE Int. Conf. Fuzzy Systems, Budapest, Hungary.
11. Mendel, J. M. (2001). Uncertain Rule-Based Fuzzy Logic System: Introduction and New Directions, Prentice Hall, Upper Saddle River.
12. Mendel, J. M. and R. I. John (2002). "Type-2 fuzzy sets made simple," IEEE Trans. On Fuzzy Systems, vol.10, no.2, pp. 117-127.
13. Mendel J. M (2004). "Computing derivatives in interval type-2 fuzzy logic system," IEEE Trans. On Fuzzy Systems, vol.12. no.1, pp. 84-98.
14. Wang, C. H, C. S. Cheng, and T. T. Lee (2004). "Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)," IEEE Trans. on Syst., , Man, and Cyber. - Part B: Cybernetics, vol.34, no.3, pp. 1462-1477.