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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2022

16.09.2021

State Estimation for Genetic Regulatory Networks with Two Delay Components by Using Second-Order Reciprocally Convex Approach

verfasst von: A. Chandrasekar, T. Radhika, Quanxin Zhu

Erschienen in: Neural Processing Letters | Ausgabe 1/2022

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Abstract

This paper presents a state estimation for genetic regulatory networks with two delay components by using second-order reciprocally convex approach. The additive time delay plays an independence and the variation of the delay components with general type of lyapunov functional. The concerned results are dependent on the leakage time varying delay and upper bound of time-varying delays with derivatives. Our aim is to design a distributed state estimator which approximates the genetic states through the measurements of the sensors that is the estimation error system is asymptotically stable. In addition, the key role of this paper is derived by the second-order reciprocally convex approach. Then a genetic state estimator is designed in terms of the solution to a set of linear matrix inequalities (LMIs) which can be solved by using available software. Finally, a numerical example is employed to demonstrate the effectiveness of the proposed genetic state estimation approach.
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Metadaten
Titel
State Estimation for Genetic Regulatory Networks with Two Delay Components by Using Second-Order Reciprocally Convex Approach
verfasst von
A. Chandrasekar
T. Radhika
Quanxin Zhu
Publikationsdatum
16.09.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10633-4

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