• A one-layer recurrent neural n...

Treffer: A one-layer recurrent neural network for robust linear programming subject to l ∞ norm uncertainty.

Title:
A one-layer recurrent neural network for robust linear programming subject to l ∞ norm uncertainty.
Authors:
Hu J; School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China. Electronic address: jhu@cqjtu.edu.cn., Zhou K; School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China. Electronic address: zky13536003576@163.com., Wang J; Department of Computer Science and Departmebnt of Data Science, City University of Hong Kong, Hong Kong. Electronic address: jwang.cs@cityu.edu.hk.
Source:
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Feb; Vol. 194, pp. 108144. Date of Electronic Publication: 2025 Sep 26.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York : Pergamon Press, [c1988-
MeSH Terms:
Neural Networks, Computer* , Programming, Linear*, Uncertainty ; Algorithms ; Computer Simulation ; Recurrent Neural Networks
Contributed Indexing:
Keywords: Neurodynamic optimization; Nonsmooth optimization; Norm uncertainty; Robust linear programming
Entry Date(s):
Date Created: 20251003 Date Completed: 20251216 Latest Revision: 20251216
Update Code:
20251216
DOI:
10.1016/j.neunet.2025.108144
PMID:
41039681
Database:
MEDLINE

Weitere Informationen

Robust optimization problems subject to norm uncertainty appear in numerous applications in various fields such as engineering, logistics, and finance. Despite its importance, robust optimization algorithms face significant computational challenges for solving high-dimensional problems, limiting their practical use. This paper presents a neurodynamic approach to mitigate these challenges by transforming the robust linear programming to a non-smooth convex optimization through parameter elimination. A one-layer projection neural network with proven stability and convergence is proposed to solve the non-smooth optimization problem. The effectiveness of this approach is validated based on simulations of numerical examples and applications in reactor design and wastewater treatment.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.