Exponential H∞ stable learning method for Takagi-Sugeno fuzzy delayed neural networks: A convex optimization approach

Computers and Mathematics with Applications

Volumn 63, Issue 5, 2012, Pages 887-895

  Ahn, Choon Ki ^a  

^a Seoul National University of Science and Technology   (South Korea)

Author keywords

Exponential H stability; Linear matrix inequality (LMI); Lyapunov Krasovskii approach; Networks; Takagi Sugeno fuzzy delayed neural; Weight learning method

Indexed keywords

CONVEX OPTIMIZATION; FUZZY INFERENCE; FUZZY LOGIC; LEARNING SYSTEMS; LINEAR MATRIX INEQUALITIES; MATRIX ALGEBRA; NETWORKS (CIRCUITS); NEURAL NETWORKS; OPTIMIZATION; ROBUSTNESS (CONTROL SYSTEMS); TIME DELAY;

CONVEX OPTIMIZATION PROBLEMS; DELAYED NEURAL NETWORKS; EXPONENTIAL H; EXTERNAL DISTURBANCES; LEARNING METHODS; LYAPUNOV-KRASOVSKII APPROACH; OPTIMIZATION APPROACH; TAKAGI-SUGENO;

FUZZY NEURAL NETWORKS;

EID: 84865616192     PISSN: 08981221     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.camwa.2011.11.054     Document Type: Article

References (23)

1. M.M. Gupta, L. Jin, N. Homma, Static and Dynamic Neural Networks, Wiley-Interscience, 2003
2. L. Jin, M.M. Gupta, Stable dynamic backpropagation learning in recurrent neural networks, IEEE Trans. Neural Netw. 10 (1999) 1321-1334 (Pubitemid 30536593)
3. T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Syst. Man Cybern. 15 (1985) 116-132
4. K. Tanaka, M. Sugeno, Stability analysis and design of fuzzy control systems, Fuzzy Sets Syst. 45 (1992) 135-156
5. H. Huang, D.W.C. Ho, J. Lam, Stochastic stability analysis of fuzzy Hopfield neural networks with time-varying delays, IEEE Trans. Circuits Syst. II 52 (2005) 251-255 (Pubitemid 40794924)
6. M.S. Ali, P. Balasubramaniam, Stability analysis of uncertain fuzzy Hopfield neural networks with time delays, Commun. Nonlinear Sci. Numer. Simul. 14 (2009) 2776-2783
7. H. Li, B. Chen, Q. Zhou, W. Qian, Robust stability for uncertain delayed fuzzy Hopfield neural networks with Markovian jumping parameters, IEEE Trans. Syst. Man Cybern. B 39 (2009) 94-102
8. X.B. Liang, L.D. Wu, A simple proof of a necessary and sufficient condition for absolute stability of symmetric neural networks, IEEE Trans. Circuits Syst. I 45 (1998) 1010-1011
9. E.N. Sanchez, J.P. Perez, Input-to-state stability (ISS) analysis for dynamic neural networks, IEEE Trans. Circuits Syst. I 46 (1999) 1395-1398 (Pubitemid 30519700)
10. T. Chu, C. Zhang, Z. Zhang, Necessary and sufficient condition for absolute stability of normal neural networks, Neural Netw. 16 (2003) 1223-1227 (Pubitemid 37103173)
11. T. Chu, C. Zhang, New necessary and sufficient conditions for absolute stability of neural networks, Neural Netw. 20 (2007) 94-101 (Pubitemid 44960620)
12. G.A. Rovithakis, M.A. Christodoulou, Adaptive control of unknown plants using dynamical neural networks, IEEE Trans. Syst. Man Cybern. 24 (1994) 400-412
13. S. Jagannathan, F.L. Lewis, Identification of nonlinear dynamical systems using multilayered neural networks, Automatica 32 (1996) 1707-1712 (Pubitemid 126647295)
14. J.A.K. Suykens, J. Vandewalle, B. De Moor, Lure systems with multilayer perceptron and recurrent neural networks; absolute stability and dissipativity, IEEE Trans. Automat. Control 44 (1999) 770-774
15. W. Yu, X. Li, Some stability properties of dynamic neural networks, IEEE Trans. Circuits Syst. I 48 (2001) 256-259 (Pubitemid 32254620)
16. I. Chairez, A. Poznyak, T. Poznyak, New sliding-mode learning law for dynamic neural network observer, IEEE Trans. Circuits Syst. II 53 (2006) 1338-1342 (Pubitemid 46443148)
17. W. Yu, X. Li, Passivity analysis of dynamic neural networks with different time-scales, Neural Process. Lett. 25 (2007) 143-155 (Pubitemid 46327488)
18. J.J. Rubio, W. Yu, Nonlinear system identification with recurrent neural networks and dead-zone Kalman filter algorithm, Neurocomputing 70 (2007) 2460-2466 (Pubitemid 46825358)
19. Anton Stoorvogel, The H∞ Control Problem: A State-Space Approach, Prentice Hall, International, UK, 1992
20. K. Nishiyama, K. Suzuki, H∞ learning of layered neural networks, IEEE Trans. Neural Netw. 12 (2001) 1265-1277
21. S. Boyd, L.E. Ghaoui, E. Feron, V. Balakrishnan, Linear Matrix Inequalities in Systems and Control Theory, SIAM, Philadelphia, PA, 1994
22. E. Noldus, Stabilization of a class of distributional convolutional equations, Int. J. Control 41 (1985) 947-960 (Pubitemid 15527730)
23. P. Gahinet, A. Nemirovski, A.J. Laub, M. Chilali, LMI Control Toolbox, The Mathworks Inc., 1995.