Stability criteria for three-layer locally recurrent networks

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 17, Issue 1 PART 1, 2008, Pages

  Patan, Krzysztof ^a  

^a NONE

Author keywords

Modelling; Neural networks technology

Indexed keywords

DISCRETE TIME RECURRENT NEURAL NETWORK; DYNAMIC NEURONS; FEED-FORWARD; GLOBAL STABILITY; LYAPUNOV; MODELLING; RECURRENT NETWORKS; SINGLE NEURON; THREE-LAYER;

RECURRENT NEURAL NETWORKS;

STABILITY CRITERIA;

EID: 79961018093     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20080706-5-KR-1001.2210     Document Type: Conference Paper

References (20)

1. S. Boyd, L. E. Ghaoui, E. Feron, and V. Balakrishnan. Linear Matrix Inequalities in System and Control Theory . SIAM Studies in Applied and Numerical Mathematics. SIAM, Philadelphia, USA, 1994. Vol.15.
2. P. Campolucci and F. Piazza. Intrinsic stability-control method for recursive filters and neural networks. IEEE Trans. Circuit and Systems ? II: Analog and Digital Signal Processing , 47(8):797-802, 2000.
3. B. Cannas, S. Cincotti, M. Marchesi, and F. Pilo. Learnig of Chua?s circuit attractors by locally recurrent neural networks. Chaos Solitons & Fractals , 12:2109-2115, 2001.
4. J. Cao, K. Yuan, and H.X. Li. Global asymptotical stability of recurrent neural networks with multiple discrete delays and distributed delays. IEEE Trans. Neural Networks, 17:1646-1651, 2006.
5. T. Ensari and S. Arik. Global stability analysis of neural networks with multiple time varying delays. IEEE Trans. Automatic Control, 50:1781-1785, 2005.
6. Y. Fang and T. G. Kincaid. Stability analysis of dynamical neural networks. IEEE Trans. Neural Networks, 7:996-1006, 1996.
7. M. Forti, P. Nistri, and D. Papini. Global exponential stability and global convergence in finite time of delayed neural networks with infinite gain. IEEE Trans. Neural Networks, 16:1449-1463, 2005.
8. P. Gahinet and P. Apkarian. A linear matrix inequality approach to H1 control. International Journal of Robust and Nonlinear Control, 4:421-448, 1994.
9. M. M. Gupta and D. H. Rao. Dynamic neural units with application to the control of unknown nonlinear systems. Journal of Intelligent and Fuzzy Systems, 1:73-92, 1993.
10. M. M. Gupta, L. Jin, and N. Homma. Static and Dynamic Neural Neetworks. From Fundamentals to Advanced Theory. John Wiley & Sons, New Jersey, 2003.
11. S. Hu and J. Wang. Global stability of a class of discrete-time recurrent neural networks. IEEE Trans. Circuits and Systems-I: Fundamental Theory and Applications, 49:1104-1117, 2002.
12. L. Jin, P. N. Nikiforuk, and M. M. Gupta. Absolute stability conditions for discrete-time recurrent neural networks. IEEE Trans. Neural Networks, 5:954-963, 1994.
13. T. Marcu, L. Mirea, and P. M. Frank. Development of dynamical neural networks with application to observer based fault detection and isolation. International Journal of Applied Mathematics and Computer Science, 9(3):547-570, 1999.
14. K. Matsuoka. Stability conditions for nonlinear continuous neural networks with asymmetric connection weights. Neural Networks, 5:495-500, 1992.
15. K. S. Narendra and K. Parthasarathy. Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Netwoks, 1:12-18, 1990.
16. K. Patan. Stability analysis and the stabilization of a class of discrete-time dynamic neural networks. IEEE Trans. Neural Networks, 18:660-673, 2007a.
17. K. Patan. Aproximation of state-space trajectories by locally recurrent globally feed-forward neural networks. Neural Networks, 2007b. DOI: 10.1016/j.neunet.2007.10.004.
18. K. Patan and T. Parisini. Identification of neural dynamic models for fault detection and isolation: the case of a real sugar evaporation process. Journal of Process Control, 15:67-79, 2005.
19. A. Ch. Tsoi and A. D. Back. Locally recurrent globally feedforward networks: A critical review of architectures. IEEE Trans. Neural Networks, 5:229-239, 1994.
20. J. Zhang, A. J. Morris, and E. B. Martin. Long term prediction models based on mixed order locally recurrent neural networks. Computers Chem. Engng, 22:1051-1063, 1998.