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Improved Stability Criteria for Discrete-Time Delayed Neural Networks via Novel Lyapunov–Krasovskii Functionals
- Source :
- IEEE Transactions on Cybernetics. 52:11885-11892
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- This article investigates the stability problem for discrete-time neural networks with a time-varying delay by focusing on developing new Lyapunov-Krasovskii (L-K) functionals. A novel L-K functional is deliberately tailored from two aspects: 1) the quadratic term and 2) the single-summation term. When the variation of the discrete-time delay is further considered, the constant matrix involved in the quadratic term is extended to be a delay-dependent one. All these innovations make a contribution to a quadratic function with respect to the delay from the forward differences of L-K functionals. Consequently, tractable stability criteria are derived that are shown to be more relaxed than existing results via numerical examples.
- Subjects :
- Time Factors
Artificial neural network
Computer science
Stability (learning theory)
Quadratic function
Computer Science Applications
Term (time)
Human-Computer Interaction
Matrix (mathematics)
Quadratic equation
Discrete time and continuous time
Control and Systems Engineering
Applied mathematics
Neural Networks, Computer
Electrical and Electronic Engineering
Constant (mathematics)
Algorithms
Software
Information Systems
Subjects
Details
- ISSN :
- 21682275 and 21682267
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Cybernetics
- Accession number :
- edsair.doi.dedup.....771770e39c6bf9eab5514d85ef213dea