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On finite-horizon \(H_\infty\) state estimation for discrete-time delayed memristive neural networks under stochastic communication protocol. (English) Zbl 1485.93568

Summary: This paper is concerned with the protocol-based finite-horizon \(H_\infty\) estimation problem for discrete-time memristive neural networks (MNNs) subject to time-delays and energy-bounded disturbances. With the purpose of effectively alleviating data collisions and saving energy, the stochastic communication protocol (SCP) is adopted to regulate the data transmission procedure in the sensor-to-estimator communication channel, thereby avoiding unnecessary network congestion. It is our objective to construct an \(H_\infty\) estimator ensuring a prescribed disturbance attenuation level over a finite time-horizon for the delayed MNNs under the SCP. By virtue of the Lyapunov-Krasovskii functional in combination with stochastic analysis methods, the delay-dependent criteria are established that guarantee the existence of the desired \(H_\infty\) estimator. Subsequently, the estimator gains are computed by resorting to solve a bank of convex optimization problems. Finally, the validity of the designed \(H_\infty\) estimator is demonstrated via a numerical example.

MSC:

93E10 Estimation and detection in stochastic control theory
93B36 \(H^\infty\)-control
93C55 Discrete-time control/observation systems
93B70 Networked control
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