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A New Robust Stability Result for Delayed Neural Networks

  • Ozlem Faydasicok
  • Cemal Cicek
  • Sabri Arik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11302)

Abstract

This work proposes a further improved global robust stability condition for neural networks involving intervalized network parameters and including single time delay. For the sake of obtaining a new robust stability condition, a new upper bound for the norm of the intervalized interconnection matrices is established. The homeomorphism mapping and Lyapunov stability theorems are employed to derive the proposed stability condition by making use of this upper bound norm. The obtained result is applicable to all nondecreasing slope-bounded activation functions and imposes constraints on parameters of neural network without involving time delay.

Keywords

Delayed systems Neural networks Robust stability Lyapunov theorems 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of MathematicsIstanbul UniversityIstanbulTurkey
  2. 2.Department of Computer EngineeringIstanbul University-CerrahpasaIstanbulTurkey

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