Abstract
This paper is concerned with the protocol-based finite-horizon H(infinity )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-infinity 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-infinity estimator. Subsequently, the estimator gains are computed by resorting to solve a bank of convex optimization problems. Finally, the validity of the designed H-infinity estimator is demonstrated via a numerical example. (C) 2020 Elsevier Inc. All rights reserved.