Fixed-time synchronization control of fuzzy inertial neural networks with mismatched parameters and structures

This research addressed the issue of fixed-time synchronization between random neutral-type fuzzy inertial neural networks and non-random neutral-type fuzzy inertial neural networks. Notably, it should be emphasized that the parameters of the drive and reaction systems did not correspond. Initially, additional free parameters were introduced to reduce the order of the error system. Subsequently, considering the influence of memory on system dynamics, a piecewise time-delay fixed time controller was developed to compensate for the influence of the time delay on the system. Utilizing stochastic analysis techniques and Lyapunov functions, sufficient conditions were derived to ensure the random fixed-time synchronization of the two neural networks. Furthermore, the settling time for system synchronization was assessed using stochastic finite-time inequalities. As a particular case, the necessary criteria for achieving fixed-time synchronization were established when the strength of the random disturbances was equal to zero. Finally, simulation results were provided to demonstrate the effectiveness of the proposed approach.