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Global Passivity Analysis of Interval Neural Networks with Discrete and Distributed Delays of Neutral Type

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Abstract

This paper is concerned with delay-dependent passivity analysis for delayed neural networks (DNNs) of neutral type. We first discuss the passivity conditions for DNNs without uncertainties and then extend this result to the case of interval uncertainties. By partitioning the delay intervals into multiple equidistant subintervals, some appropriate Lyapunov-Krasovskii functionals (LKFs) are constructed on these intervals. Considering these new LKFs and using free-weighting matrix approach, several new passivity criteria are proposed in terms of linear matrix inequalities, which are dependent on the size of the time delay. Finally, five numerical examples are given to illustrate the effectiveness and less conservatism of the developed techniques.

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Authors and Affiliations

  1. Department of Mathematics, Gandhigram Rural University, Gandhigram, 624 302, Tamilnadu, India

    P. Balasubramaniam, G. Nagamani & R. Rakkiyappan

Authors
  1. P. Balasubramaniam
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  2. G. Nagamani
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  3. R. Rakkiyappan
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Corresponding author

Correspondence to P. Balasubramaniam.

Additional information

The work of authors was supported by Department of Science and Technology, New Delhi, India, under the sanctioned No. SR/S4/MS:485/07.

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Balasubramaniam, P., Nagamani, G. & Rakkiyappan, R. Global Passivity Analysis of Interval Neural Networks with Discrete and Distributed Delays of Neutral Type. Neural Process Lett 32, 109–130 (2010). https://doi.org/10.1007/s11063-010-9147-8

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