Stable Synchronization in Fuzzy Recurrent Neural Networks within a Fixed Time Frame

This paper explores fixed-time synchronization for discontinuous fuzzy delay recurrent neural networks (DFRNNs) with time-varying delays. Based on a generalized variable transformation, the error system has been developed to effectively manage discontinuities in neural systems. This research addresses the fixed-time stability problem using a novel discontinuous state-feedback control input and a simple switching adaptive control scheme. The proposed method ensures robust synchronization of the drive and response neural systems within a fixed time. Practical applications of this work include improvements in protocols for secure communications, robotic control systems, and intelligent control frameworks over dynamic systems. A numerical example substantiates the theoretical claims, demonstrating the strengths of the proposed approach. The results show fixed-time convergence of error margins to zero, ensuring unbiased performance within a predefined timeframe, independent of initial conditions.