Exponential stability of periodic solutions of recurrent neural networks with functional dependence on piecewise constant argument | Zendy

Marat Akhmet | Zendy; Duygu Aruğaslan Çinçin | Zendy; Nur CENGİZ | Zendy

Open Access

Exponential stability of periodic solutions of recurrent neural networks with functional dependence on piecewise constant argument

Author(s) -

Marat Akhmet,

Duygu Aruğaslan Çinçin,

Nur CENGİZ

Publication year - 2018

Publication title -

turkish journal of mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.454

H-Index - 27

eISSN - 1303-6149

pISSN - 1300-0098

DOI - 10.3906/mat-1606-138

Subject(s) - mathematics , piecewise , uniqueness , constant (computer programming) , exponential stability , bounded function , argument (complex analysis) , exponential dichotomy , mathematical analysis , stability (learning theory) , constant coefficients , differential equation , nonlinear system , physics , computer science , biochemistry , chemistry , quantum mechanics , machine learning , programming language

In this study, we develop a model of recurrent neural networks with functional dependence on piecewise constant argument of generalized type. Using the theoretical results obtained for functional differential equations with piecewise constant argument, we investigate conditions for existence and uniqueness of solutions, bounded solutions, and exponential stability of periodic solutions. We provide conditions based on the parameters of the model.

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