Non-Oscillatory Control Based Fixed-Time Synchronization of Fuzzy Memristive Neural Networks

Abstract

This paper investigates the fixed-time (FXT) synchronization issue of fuzzy memristive neural networks (MNNs) via using incomplete Beta functions from the view of improving the estimate accuracy of settling time (ST). First, the parameter mismatching issue brought by the switching characteristics of the memristor is handled through the convex analysis method. Then, a new FXT stability theorem that provides a more accurate ST estimation is derived by using incomplete Beta functions. Furthermore, based on this result, some new sufficient conditions are obtained to ensure the FXT synchronization of considered fuzzy MNNs via designing a class of control schemes by introducing a new saturation function as well as using some inequality techniques. Significantly, the introduced FXT controller can achieve synchronization aim at bounded ST and it is not affected by the system's initial values. Finally, a numerical example is provided to verify the affectivity of introduced results.