Robust state estimation for fractional-order complex-valued delayed neural networks with interval parameter uncertainties: LMI approach. (English) Zbl 1433.34011
Summary: Without separating complex-valued neural networks into two real-valued systems, the state estimation of fractional-order complex-valued neural networks (FCNNs) with uncertain parameters and time delay is investigated in this paper. Based on Lyapunov-Krasovskii functional approach, a new linear matrix inequality (LMI) criterion is derived for asymptotic stability of the estimation error system. A numerical example with simulations is given to confirm the feasibility and availability of the raised result.
MSC:
| 34A08 | Fractional ordinary differential equations |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
| 93B53 | Observers |
| 93D20 | Asymptotic stability in control theory |
| 34K20 | Stability theory of functional-differential equations |
| 93D09 | Robust stability |
Keywords:
state estimation; fractional-order; complex-valued neural networks; interval parameter uncertainty; time delayReferences:
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