Computing Time-Varying Quadratic Optimization With Finite-Time Convergence and Noise Tolerance: A Unified Framework for Zeroing Neural Network

• Published in: IEEE transaction on neural networks and learning systems Vol. 30; no. 11; pp. 3360 - 3369
• Main Authors: Xiao, Lin, Li, Kenli, Duan, Mingxing
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additive noise; Computational efficiency; Computational modeling; Computer simulation; Computing time; Convergence; Design; Design formula; Economic models; finite-time convergence; Neural networks; Noise; Noise tolerance; Optimization; quadratic optimization; Quadratic programming; Recurrent neural networks; zeroing neural network (ZNN)

Zeroing neural network (ZNN), as a powerful calculating tool, is extensively applied in various computation and optimization fields. Convergence and noise-tolerance performance are always pursued and investigated in the ZNN field. Up to now, there are no unified ZNN models that simultaneously achiev...