Finite-Time and Fixed-Time Synchronization of Inertial Cohen–Grossberg-Type Neural Networks with Time Varying Delays

• Published in: Neural processing letters Vol. 50; no. 3; pp. 2407 - 2436
• Main Authors: Aouiti, Chaouki, Assali, El Abed, Foutayeni, Youssef El
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2019; Springer Nature B.V
• Subjects: Artificial Intelligence; Complex Systems; Computational Intelligence; Computer Science; Feedback control; Inequality; Information processing; Neural networks; Neurons; Neurosciences; Ordinary differential equations; Time synchronization; 34C27; Fixed-time synchronization; Time-varying delays; 92C20; Neural networks; 37B25; Finite-time synchronization; Inertial Cohen–Grossberg-type
• ISSN: 1370-4621; 1573-773X
• DOI: 10.1007/s11063-019-10018-8

This paper is devoted to studying the finite-time and fixed-time of inertial Cohen–Grossberg type neural networks (ICGNNs) with time varying delays. First, by constructing a proper variable substitution, the original (ICGNNs) can be rewritten as first-order differential system. Second, by utilizing feedback controllers and constructing suitable Lyapunov functionals, several new sufficient conditions guaranteeing the finite-time and the fixed-time synchronization of ICGNNs with time varying delays are obtained based on different finite-time synchronization analysis techniques. The obtained sufficient conditions are simple and easy to verify. Numerical simulations are given to illustrate the effectiveness of the theoretical results.