Mode-Dependent $$H_{\infty }$$ H ∞ Filtering for Stochastic Markovian Switching Genetic Regulatory Networks with Leakage and Time-Varying Delays

Revathi, V. M.; Balasubramaniam, P.; Ratnavelu, Kuru

doi:10.1007/s00034-014-9805-6

Cited by 11 publications

(1 citation statement)

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“…However, noise has played a key role in biological systems, only few works have been reported in the literature to minimize the effect of it. Recently, H 1 filtering approach has been developed in Revathi et al (2014), Wang et al (2008) to estimate the true concentrations of network components in genetic regulatory networks. The objective of H 1 filtering is to minimize the H 1 norm of the filtering error system from noise inputs to filtering errors.…”

Section: Introductionmentioning

confidence: 99%

Delay decomposition approach to $$\mathcal {H}_{\infty }$$ H ∞ filtering analysis of genetic oscillator networks with time-varying delays

Revathi

Balasubramaniam

2016

Cogn Neurodyn

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In this paper, the H 1 filtering problem is treated for N coupled genetic oscillator networks with timevarying delays and extrinsic molecular noises. Each individual genetic oscillator is a complex dynamical network that represents the genetic oscillations in terms of complicated biological functions with inner or outer couplings denote the biochemical interactions of mRNAs, proteins and other small molecules. Throughout the paper, first, by constructing appropriate delay decomposition dependent Lyapunov-Krasovskii functional combined with reciprocal convex approach, improved delay-dependent sufficient conditions are obtained to ensure the asymptotic stability of the filtering error system with a prescribed H 1 performance. Second, based on the above analysis, the existence of the designed H 1 filters are established in terms of linear matrix inequalities with Kronecker product. Finally, numerical examples including a coupled Goodwin oscillator model are inferred to illustrate the effectiveness and less conservatism of the proposed techniques.

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Section: Introductionmentioning

confidence: 99%