Stability analysis of recurrent neural networks with piecewise constant argument of generalized type

Date

2010-09-01

Author

Akhmet, Marat
Yılmaz, Elanur

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In this paper, we apply the method of Lyapunov functions for differential equations with piecewise constant argument of generalized type to a model of recurrent neural networks (RNNs). The model involves both advanced and delayed arguments. Sufficient conditions are obtained for global exponential stability of the equilibrium point. Examples with numerical simulations are presented to illustrate the results.

Subject Keywords

Neural networks, Piecewise constant argument of generalized type

URI

https://hdl.handle.net/11511/31600

Journal

NEURAL NETWORKS

DOI

https://doi.org/10.1016/j.neunet.2010.05.006

Collections

Graduate School of Social Sciences, Article

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Citation Formats

M. Akhmet and E. Yılmaz, “Stability analysis of recurrent neural networks with piecewise constant argument of generalized type,” NEURAL NETWORKS, pp. 805–811, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31600.