Finite-Horizon $H_\infty$ State Estimation for Time-Varying Neural Networks with Periodic Inner Coupling and Measurements Scheduling

This paper investigates an
${H}_\infty $
estimator design for time-varying coupled neural networks (NNs) over a finite-horizon. In order to reduce the information exchanged among the NNs, a periodic inner-coupling strategy is proposed. In addition, a Markov driven transmission scheme is introduced to overcome the communication capacity constraint between the NNs and the estimators, where an inner-coupling-dependent Markov chain is used to improve the efficiency of the communication channel. Subsequently, the time-varying Markov estimators are designed to enhance the performance of the estimators. A recursive matrix inequality (RMI)-based sufficient condition is established to ensure that the time-varying estimation error system meets the finite-horizon
${H}_\infty $
performance. Afterward, the estimator gains are designed by transforming the RMIs into linear RMIs. Finally, a numeral example is used to illustrate the developed results.