Finite-time stability of fractional-order bidirectional associative memory neural networks with mixed time-varying delays

This paper investigates the finite-time stability of fractional-order bidirectional associative memory neural networks with mixed time-varying delays. The sufficient conditions are derived to ensure the finite-time stability of systems by employing some analytical techniques and some inequalities. I...

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Bibliographic Details
Published in:Journal of applied mathematics & computing 2020-06, Vol.63 (1-2), p.501-522
Main Authors: Yang, Zhanying, Zhang, Jie, Niu, Yanqing
Format: Article
Language:English
Subjects:
Applied mathematics
Associative memory
Computational Mathematics and Numerical Analysis
Mathematical and Computational Engineering
Mathematics
Mathematics and Statistics
Mathematics of Computing
Neural networks
Original Research
Recurrent neural networks
Stability analysis
Theory of Computation
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Summary:This paper investigates the finite-time stability of fractional-order bidirectional associative memory neural networks with mixed time-varying delays. The sufficient conditions are derived to ensure the finite-time stability of systems by employing some analytical techniques and some inequalities. In addition, some conditions are achieved to guarantee the existence, the uniqueness and the finite-time stability of equilibrium point. Finally, two numerical examples are given to verify the effectiveness of the obtained main results.
ISSN:1598-5865
1865-2085
DOI:10.1007/s12190-020-01327-6