State estimator for neural networks with sampled data using discontinuous Lyapunov functional approach
- State estimator for neural networks with sampled data using discontinuous Lyapunov functional approach
- 박주현; S. Lakshmanan[S. Lakshmanan]; R. Rakkiyappan[R. Rakkiyappan]; 정호열
- TIME-VARYING DELAY; EXPONENTIAL STABILITY; CONTROL-SYSTEMS; NEUTRAL-TYPE; DISTRIBUTED DELAYS; NONLINEAR-SYSTEMS; DESIGN; STABILIZATION; SYNCHRONIZATION; INEQUALITY
- Issue Date
- NONLINEAR DYNAMICS, v.73, no.1-2, pp.509 - 520
- In this paper, the sampled-data state estimation problem is investigated for neural networks with time-varying delays. Instead of the continuous measurement, the sampled measurement is used to estimate the neuron states, and a sampled data estimator is constructed. Based on the extended Wirtinger inequality, a discontinuous Lyapunov functional is introduced, which makes full use of the sawtooth structure characteristic of sampling input delay. New delay-dependent criteria are developed to estimate the neuron states through available output measurements such that the estimation error system is asymptotically stable. The criteria are formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of some standard numerical packages. Finally, a numerical example and its simulations are given to demonstrate the usefulness and effectiveness of the presented results.
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