Model predictive stabilizer for T-S fuzzy recurrent multilayer neural network models with general terminal weighting matrix

Neural Computing and Applications

Volumn 23, Issue SUPPL1, 2013, Pages 271-277

  Ahn, Choon Ki ^a   Lim, Myo Taeg ^a  

^a Korea University   (South Korea)

Author keywords

Cost monotonicity; Linear matrix inequality (LMI); Model predictive stabilization; Takagi Sugeno (T S) fuzzy neural networks

Indexed keywords

ASYMPTOTICALLY STABLE; COST MONOTONICITY; MODEL PREDICTIVE; RECEDING HORIZON; STABILIZATION PROBLEMS; T-S FUZZY; TAKAGISUGENO (T-S); TERMINAL WEIGHTING MATRIX;

COST FUNCTIONS; FUZZY NEURAL NETWORKS; MULTILAYER NEURAL NETWORKS; STABILIZATION;

LINEAR MATRIX INEQUALITIES;

EID: 84888819586     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-013-1381-3     Document Type: Article

References (21)

1. Hopfield J (1984) Neurons with grade response have collective computational properties like those of two-state neurons. Proc Natl Acad Sci 81: 3088-3092.
2. Gupta MM, Jin L, Homma N (2003) Static and dynamic neural networks. Wiley-Interscience, London.
3. Takagi T, M Sugeno (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15: 116-132.
4. Tanaka K, Sugeno M (1992) Stability analysis and design of fuzzy control systems. Fuzzy Sets Syst 45: 135-156.
5. Huang H, Ho D, Lam J (2005) Stochastic stability analysis of fuzzy Hopfield neural networks with time-varying delays. IEEE Trans Circ Syst II 52: 251-255.
6. Ali M, Balasubramaniam P (2009) Stability analysis of uncertain fuzzy Hopfield neural networks with time delays. Commun. Nonlinear Sci Numer Simul 14: 2776-2783.
7. Li H, Chen B, Zhou Q, Qian W (2009) Robust stability for uncertain delayed fuzzy Hopfield neural networks with markovian jumping parameters. IEEE Trans Syst Man Cybern B 39: 94-102.
8. Ahn CK (2010) Delay-dependent state estimation for T-S fuzzy delayed Hopfield neural networks. Nonlinear Dyn 61(3): 483-489.
9. Ahn CK (2011) Some new results on stability of Takagi-Sugeno fuzzy Hopfield neural networks. Fuzzy Sets and Syst 179(1): 100-111.
10. ∞ nonlinear system identification. Neural Processing Lett 34(1): 59-70.
11. ∞ state estimation for Takagi-Sugeno fuzzy delayed Hopfield neural networks. Int J Comput Intell Syst 4(5): 855-862.
12. ∞ stable learning method for Takagi-Sugeno fuzzy delayed neural networks: a convex optimization approach. Comput Math Appl 64(5): 887-895.
13. ∞ stability of Takagi-Sugeno fuzzy systems combined with Hopfield neural networks. Int J Innov Comput Inf Control (in press).
14. Kwon WH, Pearson AE (1977) A modified quadratic cost problem and feedback stabilization of a linear system. IEEE Trans Autom Control 22: 838-842.
15. Kwon WH, Pearson AE (1978) On feedback stabilization of time-varying discrete linear systems. IEEE Trans Autom Control 23: 479-481.
16. Keerthi SS, Gilbert EG (1988) Optimal infinite horizon feedback laws for a general class of constrained discrete-time systems: stability and moving-horizon approximation. J Optim Theory Appl 57: 265-293.
17. Kothare MV, Balakrishnan V, Morari M (1996) Robust constrained model predictive control using linear matrix inequalities. Automatica 32: 1361-1379.
18. Chen H, Allgower F (1998) A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability. Automatica 34: 1205-1217.
19. Kwon WH, Kim KB (2000) On stabilizing receding horizon controls for linear continuous time-invariant systems. IEEE Trans Autom Control 45: 1329-1334.
20. Boyd S, Ghaoui LE, Feron E, Balakrishinan V (1994) Linear matrix inequalities in systems and control theory. SIAM, Philadelphia, PA.
21. ∞ chaotic synchronization and its application to Lorenz system. Nonlinear Anal Hybrid Syst 9: 1-8.