A zeroing feedback gradient-based neural dynamics model for solving dynamic quadratic programming problems with linear equation constraints in finite time

Gradient-based neural dynamics (GND) models are a classical algorithm for solving optimization problems, but it has non-negligible flaws in solving dynamic problems. In this study, a novel GND model, namely the zeroing feedback gradient-based neural dynamics (ZF-GND) models, is proposed based on the...

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Bibliographic Details
Published in:Neural computing & applications 2024-09, Vol.36 (26), p.16395-16409
Main Authors: Du, Shangfeng, Fu, Dongyang, Jin, Long, Si, Yang, Li, Yongze
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Convergence
Data Mining and Knowledge Discovery
Exact solutions
Feedback
Image Processing and Computer Vision
Lagrange multiplier
Linear equations
Linear programming
Motion planning
Neural networks
Optimization
Original Article
Probability and Statistics in Computer Science
Quadratic programming
Remote sensing
Robot dynamics
Robots
Upper bounds
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Summary:Gradient-based neural dynamics (GND) models are a classical algorithm for solving optimization problems, but it has non-negligible flaws in solving dynamic problems. In this study, a novel GND model, namely the zeroing feedback gradient-based neural dynamics (ZF-GND) models, is proposed based on the original GND model for tracking down the exact solution of dynamic quadratic programming problem (DQP). Further, a nonlinear projection function is designed to accelerate the convergence of the model. An upper bound on the convergence time of the ZF-GND model is rigorously defined through theoretical analysis. The superior effect of the ZF-GND model in terms of convergence is verified through comparison experiments. Finally, an application of robot motion planning is introduced to verify the practicality of the ZF-GND model.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-024-09762-3