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Finite-time \(\mathcal{H}_\infty\) asynchronous state estimation for discrete-time fuzzy Markov jump neural networks with uncertain measurements. (English) Zbl 1423.93381
Summary: This paper is concerned with the problem of the \(\mathcal{H}_\infty\) asynchronous state estimation for fuzzy Markov jump neural networks (FMJNNs) with uncertain measurements over a finite-time interval. In terms of a Bernoulli distributed white sequence, the phenomenon of the randomly occurring uncertainties in the output equation is represented by exploiting a random variable with known occurrence probabilities. The main focus of this paper is to present a state estimator such that the resulting error system is finite-time bounded and satisfies an \(\mathcal{H}_\infty\) performance requirement. Then, by employing the stochastic analysis technique, sufficient conditions are provided to ensure that the state estimator is designed by means of solving a convex optimization problem. An example is finally given to explain the effectiveness and potentiality of the proposed design method.

93E10 Estimation and detection in stochastic control theory
93A14 Decentralized systems
93B36 \(H^\infty\)-control
93C42 Fuzzy control/observation systems
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