Enhancing synchronization criteria for fractional-order chaotic neural networks via intermittent control: an extended dissipativity approach
Saravanan Shanmugam, Rajarathinam Vadivel, S. Sabarathinam, Porpattama Hammachukiattikul, Nallappan Gunasekaran · Mathematical Modelling and Control · 2025
In this paper, a recurrent intermittent control (RIC) for the synchronization of fractional-order chaotic neural networks (FOCNNs) is proposed in view of the extended dissipativity-based approach. Successively, standard linear matrix inequalites (LMIs)-based extended dissipative criteria are derived through differential inclusions and inequality mechanisms. Several sufficient conditions are obtained to ensure the synchronization of FOCNNs. Furthermore, RIC is generated to solve the synchronization problem for the considered FOCNNs. Based on the piecewise Lyapunov functional, this paper derives a exponentially stable criterion in connection with linear matrix inequalities using the Matlab toolbox. Extended dissipativity can be employed to precisely define $ L_2 $–$ L_{\infty} $, $ H_{\infty} $, passivity, and $ (Q, S, R) $-$ \vartheta $ dissipative performance. This is achieved by modifying the weighting matrices to achieve the desired performance level. The successful application of the stability criterion that was planned is demonstrated by the outcomes of the simulation.