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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.

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