Globally Robust Stability Analysis for Stochastic Cohen–Grossberg Neural Networks with Impulse Control and Time-Varying Delays

Globally Robust Stability Analysis for Stochastic Cohen–Grossberg Neural Networks with Impulse Control and Time-Varying Delays

By constructing suitable Lyapunov functionals, in combination with the matrix-inequality technique, we establish a new simple sufficient linear matrix-inequality condition for the global robustly asymptotic stability of the stochastic Cohen–Grossberg neural networks with impulsive control and time-v...

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Veröffentlicht in:Ukrainian mathematical journal 2018, Vol.69 (8), p.1220-1233
1. Verfasser: Guo, Y.
Format: Artikel
Sprache:eng
Schlagworte:
Algebra
Analysis
Applications of Mathematics
Artificial neural networks
Control stability
Functionals
Geometry
Mathematical analysis
Mathematics
Mathematics and Statistics
Matrix methods
Neural networks
Stability analysis
Statistics
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Zusammenfassung:By constructing suitable Lyapunov functionals, in combination with the matrix-inequality technique, we establish a new simple sufficient linear matrix-inequality condition for the global robustly asymptotic stability of the stochastic Cohen–Grossberg neural networks with impulsive control and time-varying delays. This condition contains and improves some previous results from the earlier references.
ISSN:0041-5995
1573-9376
DOI:10.1007/s11253-017-1426-3