Stability analysis of neural-network interconnected systems

This paper is concerned with the stability problem of a neural-network (NN) interconnected system which consists of a set of NN models. First, a linear difference inclusion (LDI) state-space representation is established for the dynamics of each NN model. Subsequently, based on the LDI state-space r...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2003-01, Vol.14 (1), p.201-208
Main Authors: Hwang, Jiing-Dong, Hsiao, Feng-Hsiag
Format: Article
Language:English
Subjects:
Asymptotic stability
Biological neural networks
Control systems
Interconnected systems
Neural networks
Neurons
Numerical simulation
Stability analysis
Stability criteria
Transfer functions
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Summary:This paper is concerned with the stability problem of a neural-network (NN) interconnected system which consists of a set of NN models. First, a linear difference inclusion (LDI) state-space representation is established for the dynamics of each NN model. Subsequently, based on the LDI state-space representation, a stability criterion in terms of Lyapunov's direct method is derived to guarantee the asymptotic stability of NN interconnected systems. Finally, a numerical example with simulations is given to demonstrate the results.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2002.806643