Event-triggered H state estimation for discrete-time neural networks with mixed time delays and sensor saturations
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abstract
2016, The Natural Computing Applications Forum. In this paper, the event-triggered H state estimation problem is investigated for a class of neural networks with mixed time delays and sensor saturations. The mixed time delays consist of discrete and distributed delays. The measurement outputs are subject to the sensor saturations due to the physical constraints. Through the available measurement outputs, the main purpose of the addressed problem is to design a state estimator to estimate the actual neural states. In order to improve the efficiency in resource utilization, an event-triggered mechanism is employed to decide whether the received measurement output is transmitted to the state estimator. Different from the existing event-triggering strategies, the triggering condition is given for each sensor, and the measurement output from each sensor is sent according to their separate triggering conditions. By using the Lyapunov functional approach, sufficient conditions are derived to guarantee that the estimation error dynamics is exponentially stable and the H performance requirement is satisfied. Then, the desired H state estimator is designed in terms of the solution to a linear matrix inequality that can be easily solved by the MATLAB toolboxes. Finally, one simulation example is provided to show the effectiveness of the proposed event-triggered estimation scheme.
