Convergence analysis on inertial proportional delayed neural networks. (English) Zbl 1482.68218
Summary: This article mainly explores a class of inertial proportional delayed neural networks. Abstaining reduced order strategy, a novel approach involving differential inequality technique and Lyapunov function fashion is presented to open out that all solutions of the considered system with their derivatives are convergent to zero vector, which refines some previously known research. Moreover, an example and its numerical simulations are given to display the exactness of the proposed approach.
MSC:
| 68T07 | Artificial neural networks and deep learning |
| 34K13 | Periodic solutions to functional-differential equations |
| 34K20 | Stability theory of functional-differential equations |
| 34K25 | Asymptotic theory of functional-differential equations |
Keywords:
inertial neural networks; global convergence; proportional delay; non-reduced order strategyReferences:
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