New discrete-time recurrent neural network proposal for quadratic optimization with general linear constraints.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 24808285.
- Also identified by DOI 10.1109/TNNLS.2012.2223484.
- 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
In this brief, the quadratic problem with general linear constraints is reformulated using the Wolfe dual theory, and a very simple discrete-time recurrent neural network is proved to be able to solve it. Conditions that guarantee global convergence of this network to the constrained minimum are developed. The computational complexity of the method is analyzed, and experimental work is presented that shows its high efficiency.
Medical subject headings
- Neural Networks, Computer
- Programming, Linear