Abstract
The stability of a neural network model may often be destroyed by the parameter deviations during the implementation. However, few results (if any) for the stability of such system with a certain deviation rate have been reported in the literature. In this paper, we present a simple delayed neural network model, in which each parameter deviates the reference point with a rate, and further investigate the robust exponential stability of this model and illustrate the relationship between the permissible fluctuation rate and the exponential convergence rate.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Zhang, Y., Heng, P.-H., Vadakkepat, P.: Absolute Periodicity and Absolute Stability of Delayed Neural Networks. IEEE Transactions on CAS-I 49, 256–261 (2002)
He, H., Cao, J., Wang, J.: Global Exponential Stability and Periodic Solutions of Recurrent Neural Networks with Delays. Physics Lett. A 198, 393–404 (2002)
Chen, T.: Global Exponential Stability of Delayed Hopfield Neural Networks. Neural Networks 14, 977–980 (2001)
Liao, X.: Stability of Hopfield-Type Neural Networks (I). Science in China (Scientia Sinica) Series A 14, 407–418 (1995)
Zhao, H.: Global Stability of Neural Networks with Distributed Delays. Phys. Rev. E 68, 051909 (2003)
Li, C., Chen, G.: Stability of a Neural Network Model with Small-World Connections. Phys. Rev. E 68, 052901 (2003)
Guo, S., Huang, L.: Stability Analysis of a Delayed Hopfield Neural Network. Phys. Rev. E 67, 061902 (2003)
Liao, X., Wong, K.-W.: Global Exponential Stability of Hybrid Bidirectional Associative Memory Neural Networks with Discrete Delays. Phys. Rev. E 67, 042901 (2003)
Zhang, Y., TanK, K.: Dynamic Stability Conditions for Lotka-Volterra Recurrent Neural Networks with Delays. Phys. Rev. E 66, 011910 (2002)
Liao, X., Yu, J.: Robust Stability for Interval Hopfield Neural Networks with Time Delay. IEEE Trans. Neural Networks 9, 1042–1046 (1998)
Liao, X., Wong, K.-W., Wu, Z., Chen, G.: Novel Robust Stability Criteria for Interval- Delayed Hopfield Neural Networks. IEEE Trans. Circuits Syst. I 48, 1355–1359 (2001)
Liao, X., Wang, J.: Global and Robust Stability of Interval Hopfield Neural Networks with Time-Varying Delays. Int. J. neural syst. 13, 171–182 (2003)
Arik, S.: Global Robust Stability of Delayed Neural Networks. IEEE Trans. Circuits Syst. I 50, 156–160 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tang, H., Li, C., Liao, X. (2004). Exponential Stability Analysis for Neural Network with Parameter Fluctuations. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-28647-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
eBook Packages: Springer Book Archive