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Multistability of Fractional-Order Neural Networks With Unbounded Time-Varying Delays
- Source :
- IEEE Transactions on Neural Networks and Learning Systems. 32:177-187
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- This article addresses the multistability and attraction of fractional-order neural networks (FONNs) with unbounded time-varying delays. Several sufficient conditions are given to ensure the coexistence of equilibrium points (EPs) of FONNs with concave-convex activation functions. Moreover, by exploiting the analytical method and the property of the Mittag-Leffler function, it is shown that the multiple Mittag-Leffler stability of delayed FONNs is derived and the obtained criteria do not depend on differentiable time-varying delays. In particular, the criterion of the Mittag-Leffler stability can be simplified to M-matrix. In addition, the estimation of attraction basin of delayed FONNs is studied, which implies that the extension of attraction basin is independent of the magnitude of delays. Finally, three numerical examples are given to show the validity of the theoretical results.
- Subjects :
- Equilibrium point
Time Factors
Artificial neural network
Mathematics::Complex Variables
Computer Networks and Communications
Mathematics::Classical Analysis and ODEs
Reproducibility of Results
02 engineering and technology
Function (mathematics)
Stability (probability)
Computer Science Applications
Mathematics::Probability
Exponential stability
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Applied mathematics
Computer Simulation
020201 artificial intelligence & image processing
Neural Networks, Computer
Differentiable function
Algorithms
Software
Multistability
Numerical stability
Mathematics
Subjects
Details
- ISSN :
- 21622388 and 2162237X
- Volume :
- 32
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Networks and Learning Systems
- Accession number :
- edsair.doi.dedup.....4b202e9d29fa37eafe1c41d79535969c