Improved Global Robust Stability of Interval Delayed Neural Networks Via Split Interval: Generalizations
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Date
2008
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Elsevier Science inc
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Abstract
dThe problem of global robust stability of Hop field-type delayed neural networks with the intervalized network parameters is revisited. Recently, a computationally tractable, i.e., linear matrix inequality (LMI) based global robust stability criterion derived from an earlier criterion based on dividing the given interval into more that two intervals has been presented. In the present paper, generalizations, i.e., division of the given interval into m intervals (where m is an integer greater than or equal to 2) is considered and some new LMI-based global robust stability criteria are derived. It is shown that, in some cases, m = 2 may not suffice, i.e., m > 2 may be needed to realize the improvement. An example showing the effectiveness of the proposed generalization is given. The paper also provides a complete and systematic explanation of the "split interval" idea. (c) 2008 Elsevier Inc. All rights reserved.
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Keywords
Dynamical interval neural networks, Equilibrium analysis, Global robust stability, Hopfield neural networks, Neural networks, Nonlinear systems, Time-delay systems
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Citation
15
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Q1
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Volume
206
Issue
1
Start Page
290
End Page
297