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This article is concerned with the problem of finite-time   $\mathcal {H}_{\infty }$H  &#x221E;state estimation for switched genetic regulatory networks with randomly occurring uncertainties. The persistent dwell-time switching rule, as a more versatile class of switching rules, is considered in this paper. Besides, several random variables that obey the Bernoulli distribution are used to represent randomly occurring uncertainties. The overriding purpose of this article is to design an estimator to ensure that the estimation error system is stochastically finite-time bounded and satisfies the   $\mathcal {H}_{\infty }$H  &#x221E;performance. Based on this, sufficient conditions for the explicit form of the estimator gains can be obtained by the Lyapunov method. Finally, a numerical example is given to verify the correctness and feasibility of the proposed method.

ieee/acm transactions on computational biology and bioinformatics

Finite-Time   $\mathcal {H}_{\infty }$     H  &#x221E;       State Estimation for PDT-Switched Genetic Regulatory Networks With Randomly Occurring Uncertainties