Predefined-Time Synchronization of Multiple Fuzzy Recurrent Neural Networks via a New Scaling Function.

This paper investigates the predefined-time synchronization of a group of fuzzy recurrent neural networks under a leaderless communication topology. An effective control strategy is proposed based on a time-dependent exponential function as the scaling function. Sufficient criteria for guaranteeing the predefined-time synchronization of multiple fuzzy recurrent neural networks are derived under the digraph with strong connectivity and the digraph containing spanning trees, respectively. Unlike commonly used state-dependent sign function or time-dependent power function in existing works, the scaling function in this paper is new and selected as the time-dependent exponential function. Moreover, the communication topology in this paper is assumed to be leaderless, which is distinct from the master-slave or leader-following topologies previously investigated for predefined-time synchronization. Numerical examples are provided to illustrate the correctness of results