Fixed/Prescribed-Time Synchronization of Fuzzy Inertial Memristive Neural Networks With Time-Varying Delay: Interval Matrix Method and PI Control

• Published in: IEEE access Vol. 13; pp. 70105 - 70115
• Main Authors: Ya Wang, Xin, Zhang, Qing, Chen, Guici
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological neural networks; Delay; Delays; Feedback control; fixed-time synchronization (FTS); fuzzy inertial neural networks (FINNs); Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Inclusions; interval matrix method; Linear matrix inequalities; Memristive; Memristors; Neural networks; prescribed-time synchronization (PTS); Stability analysis; Switches; Synchronization; Time synchronization; Time varying control; Time-varying systems
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2025.3561116

This paper investigates the fixed/prescribed-time synchronization issues for fuzzy inertial memristive neural networks (FIMNNs) with time-varying delay, which integrate fuzzy logic, inertial terms, memristors, and time-varying delays simultaneously. The interval matrix method is employed firstly to deal with the state-dependent switching parameters, which is distinguished from the existing maximum absolute value approach. Consequently, the FIMNNs with time-varying delay are transformed into a type of delayed fuzzy systems, with coefficients characterized by a series of interval matrices. Following this, within the framework of Filippov solutions and differential inclusions, fixed/prescribed-time synchronization is comprehensively discussed. Two novel PI feedback controllers are synthesized through the resolution of a series of linear matrix inequalities (LMIs), followed by numerical simulations to demonstrate their effectiveness.