Mode-dependent delays for dissipative filtering of stochastic semi-Markovian jump for neural networks

Abstract This work is concerned with the issue of dissipative filtering for stochastic semi-Markovian jump via neural networks where time-varying delay based upon another semi-Markov process. Dissipative performance analysis employed to solve a mode-dependent problem in unified way. To achieve this task, we implemented recently proposed notion extended dissipativity, which gives an inequality equivalent well-known $H_{\infty }$ H ∞ , $L_{2}$ L 2 – $L_{\infty and performances. Different from existing literature (Arslan et al. Neural Netw 91:11–21, 2017; Chen ISA Trans. 101:170–176, 2020) mostly delay-free filters have been investigated, our filter contains communication delay. Based delay-dependent conditions, stability dissipativity delays, results are obtained by using Lyapunov–Krasovskii functional together novel integral inequality. Original conditions characterized linear matrix inequalities. A numerical simulation elaborated elucidate feasibility design methodology.