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State-Dependent Impulsive Neural Networks

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Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS,volume 195)

Abstract

In this chapter, we address a new model of neural networks related to the discontinuity phenomena which is called state-dependent impulsive neural networks. By means of B-equivalence method, we reduce these networks to a fix time impulsive neural networks system. In the first part of this study, sufficient conditions for existence and uniqueness of exponentially stable almost periodic solution for recurrent neural networks are investigated. In the second part, sufficient conditions ensuring the existence, uniqueness and global robust asymptotic stability of the equilibrium point for a more general class of bidirectional associative memory (BAM) neural networks are obtained by employing an appropriate Lyapunov function and linear matrix inequality (LMI). Finally, an illustrative example is given to show the effectiveness of the theoretical results.

Keywords

  • Neural networks
  • State-dependent impulsive systems
  • Stability
  • Linear matrix inequality

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Şaylı, M., Yılmaz, E. (2017). State-Dependent Impulsive Neural Networks. In: Pinto, A., Zilberman, D. (eds) Modeling, Dynamics, Optimization and Bioeconomics II. DGS 2014. Springer Proceedings in Mathematics & Statistics, vol 195. Springer, Cham. https://doi.org/10.1007/978-3-319-55236-1_19

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