Asymptotically stable high-order neutral cellular neural networks with proportional delays and \(D\) operators. (English) Zbl 1510.92014
Summary: This paper aims to deal with the asymptotic stability of high-order neutral cellular neural networks (HNCNNs) incorporating proportional delays and \(D\) operators. Employing Lyapunov method, inequality technique and concise mathematical analysis proof, sufficient criteria on the global exponential asymptotical stability of the proposed HNCNNs are obtained. The main results provide us some light for designing stable HNCNNs and complement some earlier publications. In addition, simulations show that the theoretical convergence is in excellent agreement with the numerically observed behavior.
MSC:
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
| 68T07 | Artificial neural networks and deep learning |
| 34K20 | Stability theory of functional-differential equations |
| 93D23 | Exponential stability |
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