Thirunavukkarasu Radhika

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In this paper, we investigated a problem of dissipativity and passivity analysis of Markovian jump neural networks involving two additive time-varying delays. By considering proper triple integral terms in the Lyapunov–Krasovskii functional, several sufficient conditions are derived for verifying the dissipativity criteria of neural networks. The(More)
In this paper, we investigate the dissipativity and passivity of Markovian jump stochastic neural networks involving two additive time-varying delays. Using a Lyapunov-Krasovskii functional with triple and quadruple integral terms, we obtain delay-dependent passivity and dissipativity criteria for the system. Using a generalized Finsler lemma (GFL), a set(More)
This paper is concerned with strict $$(\mathcal {Q}, \mathcal {S}, \mathcal {R})-\gamma $$ ( Q , S , R ) - γ - dissipativity and passivity analysis for discrete-time Markovian jump neural networks involving both leakage and discrete delays expressed in terms of two additive time-varying delay components. The discretized Wirtinger inequality is utilized to(More)
This paper is concerned with the mixed $$H_\infty$$ H ∞ and dissipativity performance for Markovian jump neural networks with time delay in the leakage term and randomly occurring uncertainties. The randomly occurring uncertainties are assumed to be mutually uncorrelated Bernoulli-distributed white noise sequences. By introducing a triple-integrable term in(More)
In this paper, based on the knowledge of memristor-based recurrent neural networks (MRNNs), the model of the stochastic MRNNs with discrete and distributed delays is established. In real nervous systems and in the implementation of very large-scale integration (VLSI) circuits, noise is unavoidable, which leads to the stochastic model of the MRNNs. In this(More)
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