A Trust-Region Projection Neural Network for Nonlinear Programming.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 40504715.
- Also identified by DOI 10.1109/TNNLS.2025.3576600.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
The trust-region method and projection neural networks are two branches of optimization approaches with different operational principles and characteristics. In this article, a trust-region projection neural network (TRPNN) is proposed by integrating the trust-region method and projection neural networks. TRPNN is a discrete-time neurodynamic optimization model that inherits the exploration-exploitation capability of the trust-region method and the local search capability of projection neural networks. TRPNN is theoretically proven to be convergent to a Karush-Kuhn-Tuchker (KKT) point of nonlinear programming problems. The efficacy of TRPNNs leveraged in a collaborative neurodynamic framework is numerically demonstrated for global optimization in the presence of nonconvexity in objective functions or constraints.