On the global output convergence of a class of recurrent neural networks with time-varying inputs

Neural Networks

Volumn 18, Issue 2, 2005, Pages 171-178

  Hu, Sanqing ^a   Liu, Derong ^a  

^a 1120 Science and Engineering Offices   (United States)

Author keywords

Global output convergence; Lipschitz continuity; Lyapunov diagonal stability; Optimization; Recurrent neural networks; Time varying input

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; FUNCTIONS; MATRIX ALGEBRA; TIME VARYING SYSTEMS;

GLOBAL OUTPUT CONVERGENCE; LIPSCHITZ CONTINUOUS TIME-VARYING INPUTS; MONOTONE NONDECREASING ACTIVATION FUNCTIONS;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; PRIORITY JOURNAL; REACTION TIME;

ALGORITHMS; COMPUTER SIMULATION; FEEDBACK; HUMANS; LINEAR MODELS; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); NEURONS; REACTION TIME; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 15944403105     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2004.10.005     Document Type: Article

References (17)

1. M. Forti, and A. Tesi New condition for global stability of neural networks with application to linear and quadratic programming problems IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications 42 1995 354 366
2. S. Grossberg Nonlinear neural networks: Principles, mechanism, and architectures Neural Networks 1 1988 17 61
3. M.W. Hirsch Convergent activation dynamics in continuous time networks Neural Networks 2 1989 331 349
4. J.J. Hopfield Neuronal networks and physical systems with emergent collective computational abilities Proceedings of the National Academy of Sciences USA 79 1982 2554 2558
5. J.J. Hopfield Neurons with graded response have collective computational properties like those of two-state neurons Proceedings of the National Academy of Sciences USA 81 1984 3088 3092
6. X.B. Liang, and J. Si Global exponential stability of neural networks with globally Lipschitz continuous activations and its application to linear variational inequality problem IEEE Transactions on Neural Networks 12 2001 349 359
7. X.B. Liang, and L.D. Wu New sufficient conditions for absolute stability of neural networks IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications 45 1998 584 586
8. X.B. Liang, and L.D. Wu Global exponential stability of a class of neural circuits IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications 46 1999 748 751
9. Liu, D., Hu, S., & Wang, J. (2004), Global output convergence of a class of continuous-time recurrent neural networks with time-varying thresholds. IEEE Transactions on Circuits and Systems-II: Express Briefs, 51, 161-167.
10. A.N. Michel, and R.K. Miller Qualitative analysis of large scale dynamical systems in the mathematical science and engineering 1977 Academic Press New York
11. J. Wang Analog neural networks for solving the assignment problem Electronics Letters 28 1992 1047 1050
12. J. Wang Analysis and design of a recurrent neural network for linear programming IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications 40 1993 613 618
13. J. Wang A deterministic annealing neural network for convex programming Neural Networks 7 1994 629 641
14. J. Wang Analysis and design of an analog sorting network IEEE Transactions on Neural Networks 6 1995 962 971
15. J. Wang A recurrent neural network for solving the shortest path problem IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications 43 1996 482 486
16. J. Wang Primal and dual assignment networks IEEE Transactions on Neural Networks 8 1997 784 790
17. Y. Zhang, P.A. Heng, and A.W.C. Fu Estimate of exponential convergence rate and exponential stability for neural networks IEEE Transactions on Neural Networks 10 1999 1487 1493