A Dynamic Gain Fixed-Time Robust ZNN Model for Time-Variant Equality Constrained Quaternion Least Squares Problem With Applications to Multiagent Systems

A dynamic gain fixed-time (FXT) robust zeroing neural network (DFTRZNN) model is proposed to effectively solve time-variant equality constrained quaternion least squares problem (TV-EQLS). The proposed approach surmounts the shortcomings of conventional numerical algorithms which fail to address tim...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2023-10, Vol.35 (12), p.18394-18403
Hauptverfasser:	Cao, Penglin, Xiao, Lin, He, Yongjun, Li, Jichun
Format:	Artikel
Sprache:	eng
Schlagworte:	Computational modeling Convergence Fixed-time (FXT) stability Mathematical models multiagent systems Neural networks Numerical models Quaternions Robustness time-variant equality constrained quaternion least squares problem (TV-EQLS) zeroing neural network
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