A study on ⟨(Q,S,R)-γ⟩-dissipative synchronisation of coupled reaction–diffusion neural networks with time-varying delays
Muhammed Syed Ali, Quanxin Zhu, S. Pavithra, Nallappan Gunasekaran · International Journal of Systems Science · 2018
This study examines the problem of dissipative synchronisation of coupled reaction–diffusion neural networks with time-varying delays. This paper proposes a complex dynamical network consisting of N linearly and diffusively coupled identical reaction–diffusion neural networks. By constructing a suitable Lyapunov–Krasovskii functional (LKF), utilisation of Jensen's inequality and reciprocally convex combination (RCC) approach, strictly 〈(Q,S,R)-γ〉-dissipative conditions of the addressed systems are derived. Finally, a numerical example is given to show the effectiveness of the theoretical results.