Stability analysis of dynamical neural networks

Yuguang Fang; Thomas G. Kincaid

doi:10.1109/72.508941

Stability analysis of dynamical neural networks

Yuguang Fang, Thomas G. Kincaid

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

156 Citations (Scopus)

Abstract

In this paper, we use the matrix measure technique to study stability of dynamical neural networks. Testable conditions for global exponential stability of nonlinear dynamical systems and dynamical neural networks are given. It shows how a few well-known results can be unified and generalized in a straightforward way. Local exponential stability of a class of dynamical neural networks is also studied; we point out that the local exponential stability of any equilibrium point of dynamical neural networks is equivalent to the stability of the linearized system around that equilibrium point. From this, some well-known and new, sufficient conditions for local exponential stability of neural networks are obtained. © 1996 IEEE.

Original language	English
Pages (from-to)	996-1006
Journal	IEEE Transactions on Neural Networks
Volume	7
Issue number	4
DOIs	https://doi.org/10.1109/72.508941
Publication status	Published - 1996
Externally published	Yes

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Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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title = "Stability analysis of dynamical neural networks",

abstract = "In this paper, we use the matrix measure technique to study stability of dynamical neural networks. Testable conditions for global exponential stability of nonlinear dynamical systems and dynamical neural networks are given. It shows how a few well-known results can be unified and generalized in a straightforward way. Local exponential stability of a class of dynamical neural networks is also studied; we point out that the local exponential stability of any equilibrium point of dynamical neural networks is equivalent to the stability of the linearized system around that equilibrium point. From this, some well-known and new, sufficient conditions for local exponential stability of neural networks are obtained. {\textcopyright} 1996 IEEE.",

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doi = "10.1109/72.508941",

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journal = "IEEE Transactions on Neural Networks",

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AU - Fang, Yuguang

AU - Kincaid, Thomas G.

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 1996

Y1 - 1996

N2 - In this paper, we use the matrix measure technique to study stability of dynamical neural networks. Testable conditions for global exponential stability of nonlinear dynamical systems and dynamical neural networks are given. It shows how a few well-known results can be unified and generalized in a straightforward way. Local exponential stability of a class of dynamical neural networks is also studied; we point out that the local exponential stability of any equilibrium point of dynamical neural networks is equivalent to the stability of the linearized system around that equilibrium point. From this, some well-known and new, sufficient conditions for local exponential stability of neural networks are obtained. © 1996 IEEE.

AB - In this paper, we use the matrix measure technique to study stability of dynamical neural networks. Testable conditions for global exponential stability of nonlinear dynamical systems and dynamical neural networks are given. It shows how a few well-known results can be unified and generalized in a straightforward way. Local exponential stability of a class of dynamical neural networks is also studied; we point out that the local exponential stability of any equilibrium point of dynamical neural networks is equivalent to the stability of the linearized system around that equilibrium point. From this, some well-known and new, sufficient conditions for local exponential stability of neural networks are obtained. © 1996 IEEE.

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DO - 10.1109/72.508941

M3 - RGC 21 - Publication in refereed journal

SN - 1045-9227

VL - 7

SP - 996

EP - 1006

JO - IEEE Transactions on Neural Networks

JF - IEEE Transactions on Neural Networks

IS - 4

ER -

Stability analysis of dynamical neural networks

Abstract

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