Pseudo Almost Automorphic Solutions of Recurrent Neural Networks with Time-Varying Coefficients and Mixed Delays

In this paper, existence, uniqueness and global exponential stability of pseudo almost automorphic solutions for a class of recurrent neural networks with time-varying coefficients and mixed delays are established by employing the fixed point theorem and differential inequality. Numerical example wi...

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Bibliographic Details
Published in:Neural processing letters Vol. 45; no. 1; pp. 121 - 140
Main Authors: Aouiti, Chaouki, M’hamdi, Mohammed Salah, Touati, Abderrahmane
Format: Journal Article
Language:English
Published: New York Springer US 01.02.2017
Springer Nature B.V
Subjects:
Artificial Intelligence
Banach spaces
Complex Systems
Computational Intelligence
Computer Science
Fixed points (mathematics)
Neural networks
Neurons
Ordinary differential equations
Recurrent neural networks
34C27
Global exponential stability
Recurrent neural networks
92C20
Pseudo almost automorphic solution
Mixed delays
37B25
ISSN:1370-4621, 1573-773X
Online Access:Get full text
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Summary:In this paper, existence, uniqueness and global exponential stability of pseudo almost automorphic solutions for a class of recurrent neural networks with time-varying coefficients and mixed delays are established by employing the fixed point theorem and differential inequality. Numerical example with graphical illustration is given to illuminate our main results.
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ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-016-9515-0