Robust H∞ performance for discrete time T-S fuzzy switched memristive stochasticneural networks with mixed time-varying delays

• Published in: Journal of experimental & theoretical artificial intelligence Vol. 33; no. 1; pp. 79 - 107
• Main Authors: Vadivel, R., Syed Ali, M., Joo, Young Hoon
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.01.2021; Taylor & Francis Ltd
• Subjects: Artificial neural networks; Fuzzy logic; H infinity; Linear matrix inequalities; Lyapunov-Krasovskii functional; memristive; Neural networks; Parameter uncertainty; Robustness (mathematics); Switched
• ISSN: 0952-813X; 1362-3079
• DOI: 10.1080/0952813X.2020.1725649

In this paper, we study the robust H ∞ performance for discrete-time T-S fuzzy switched memristive stochastic neural networks with mixed time-varying delays and switching signal design. The neural network under consideration is subject to time-varying and norm bounded parameter uncertainties. Decomposing of the delay interval approach is employed in both the discrete delays and distributed delays. By constructing a proper Lyapunov-Krasovskii functional (LKF) with triple summation terms and using an improved summation inequality techniques. Sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to guarantee the considered discrete-time neural networks to be exponentially stable. Finally, numerical examples with simulation results are given to illustrate the effectiveness of the developed theoretical results.