This paper deals with the extended dissipativity and non-fragile synchronization of delayed recurrent neural networks (RNNs) with multiple time-varying delays and sampled-data control. A suitable Lyapunov-Krasovskii Functional (LKF) is built up to prove the quadratically stable and extended dissipativity condition of delayed RNNs using Jensen inequality and limited Bessel-Legendre inequality approaches. A non-fragile sampled-data approach is applied to investigate the problem of neural networks with multiple time-varying delays, which ensures that the master system synchronizes with the slave system and is designed with respect to the solutions of Linear Matrix Inequalities (LMIs). The effectiveness of the suggested approach is established by providing suitable simulations using MATLAB LMI control toolbox. Finally, numerical examples and comparative results are provided to illustrate the adequacy of the planned control scheme.

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.