Abstract
This paper addresses the event-triggered H∞ state estimation problem for a class of discrete recurrent neural networks subject to variance-constraint and fading measurements. The phenomena of fading measurements are described by introducing a set of mutually independent random variables, which reflect that each sensor has individual missing probability. In addition, for the purpose of energy saving, an event-triggered H∞ state estimation scheme is used for time-varying neural networks to determine whether the measurement output is transmitted to the estimator or not. Some sufficient conditions are obtained to guarantee that the estimation error system satisfies both estimation error variance constraint and prescribed H∞ performance requirement. Finally, the feasibility of the proposed event-triggered H∞ state estimation method is verified by a numerical example.
Highlights
In the past decades, the analysis and design of recursive neural networks (RNNs) have attracted great attention due to their powerful advantages, such as showing dynamic time behaviour and using internal memory to process arbitrary input sequences
The RNNs have been successfully applied to broad areas such as speech recognition, pattern recognition and associate memory
In [1], an event-based recursive input and state estimator has been designed to ensure that the covariance of the estimation error has an upper bound at any time for the linear discrete time-varying systems
Summary
The analysis and design of recursive neural networks (RNNs) have attracted great attention due to their powerful advantages, such as showing dynamic time behaviour and using internal memory to process arbitrary input sequences. Motivated by the aforementioned discussion, we handle the event-triggered H∞ state estimation problem for discrete time-varying RNNs subject to variance-constraint and fading measurements. The main work conducted can be listed: 1) the event-triggered H∞ state estimation problem is, for the first time, investigated for a class of discrete timevarying stochastic RNNs subject to variance-constraint and fading measurements; 2) a new solvable method is given for addressed variance-constrained state estimation problem based on the recursive linear matrix inequalities (RLMIs); and 3) the usual literature considers the augmented system satisfying both the prescribed H∞ performance requirement and the estimation error variance constraints, but we analyse the estimation error system directly with same order of original system, which may reduce the computational complexity
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