Absolute periodicity and absolute stability of delayed neural networks

Please use this identifier to cite or link to this item: https://doi.org/10.1109/81.983875

Title:	Absolute periodicity and absolute stability of delayed neural networks
Authors:	Yi, Z. Heng, P.A. Vadakkepat, P.
Keywords:	Absolute periodicity Absolute stability Delay Neural networks
Issue Date:	Feb-2002
Citation:	Yi, Z., Heng, P.A., Vadakkepat, P. (2002-02). Absolute periodicity and absolute stability of delayed neural networks. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 49 (2) : 256-261. ScholarBank@NUS Repository. https://doi.org/10.1109/81.983875
Abstract:	In this brief, we propose to study the absolute periodicity of delayed neural networks. A neural network is said to be absolutely periodic, if for every activation function in some suitable functional set and every input periodic vector function, a unique periodic solution of the network exists and all other solutions of the network converge exponentially to it. Absolute stability of delayed neural networks is also studied in this paper. Simple and checkable conditions for guaranteeing absolute periodicity and absolute stability are derived. Simulations for absolute periodicity are given.
Source Title:	IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
URI:	http://scholarbank.nus.edu.sg/handle/10635/54860
ISSN:	10577122
DOI:	10.1109/81.983875
Appears in Collections:	Staff Publications

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