Global dissipativity and quasi-synchronization of asynchronous updating fractional-order memristor-based neural networks via interval matrix method

This paper investigates the global dissipativity and quasi-synchronization of asynchronous updating fractional-order memristor-based neural networks (AUFMNNs) via interval matrix method. First, a new class of FMNNs named AUFMNNs is proposed for the first time, in which the switching jumps are asymmetric. In other words, each memristive connection weight is updated based on its own channel and hence the number of the subsystems increases significantly from 2n to 22n2. Under the framework of fractional-order differential inclusions, the proposed AUFMNNs can be regarded as a system with interval parameters. Then, the global dissipativity criterion is established by constructing appropriate Lyapunov function in combination with the estimates of 2-norm for interval matrices and some fractional-order differential inequalities. In addition, for drive-response AUFMNNs with mismatched parameters, the problem of quasi-synchronization is explored via linear state feedback control. It has been shown that complete synchronization between two AUFMNNs cannot be achieved via linear feedback control and that the synchronization error bound can be controlled within a relatively small level by selecting suitable control parameters. Finally, three numerical examples are given to demonstrate the effectiveness and the improvement of the obtained results.