Matrix measure strategies for stabilisation and synchronisation of complex-valued BAM neural networks
M. Yazhini, Rajendran Samidurai · Journal of Control and Decision · 2025
This study focuses on stabilisation and synchronisation of complex-valued bidirectional associative memory neural networks (CVBAMNNs) using matrix measure techniques. A novel criterion based on matrix measures is developed to guarantee the global exponential stability of the network equilibrium. To achieve global exponential synchronisation, delay-distributed dependent conditions are established by constructing Lyapunov–Krasovskii functionals (LKFs) and employing advanced inequality techniques. The effectiveness of the proposed methods is demonstrated through several mathematical modelling examples, underscoring the practical significance of CVBAMNNs in enhancing network stabilisation and synchronisation.