UKF-based remote state estimation for discrete artificial neural networks with communication bandwidth constraints

This paper is concerned with the remote state estimator design problem for a class of discrete neural networks under communication bandwidth constraints. Due to the limited bandwidth of the transmission channel, only partial components of the measurement outputs can be transmitted to the remote estimator at each time step. A UKF-based state estimator is developed to cope with the nonlinear activation functions in the neural networks subject to the communication constraints. Moreover, the stability of the proposed estimator is analyzed. Sufficient conditions are established under which the error dynamics of the state estimation is exponentially bounded in mean square. A numerical example is provided to demonstrate the effectiveness of the proposed method.