Linked Life Data

11310550

Source:http://linkedlifedata.com/resource/pubmed/id/11310550

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2001-4-19
pubmed:abstractText
When an artificial neural network (ANN) is trained to predict signals p steps ahead, the quality of the prediction typically decreases for large values of p. In this paper, we compare two methods for prediction with ANNs: the classical recursion of one-step ahead predictors and a new kind of chain structure. When applying both techniques to the prediction of the temperature at the end of a blast furnace, we conclude that the chaining approach leads to an improved prediction of the temperature and avoidance of instabilities, since the chained networks gradually take the prediction of their predecessors in the chain as an extra input. It is observed that instabilities might occur in the iterative case, which does not happen with the chaining approach. To select relevant inputs and decrease the number of weights in this approach, Automatic Relevance Determination (ARD) for multilayer perceptrons is applied.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0129-0657
pubmed:author
pubmed:issnType
Print
pubmed:volume
11
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1-10
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
Improved long-term temperature prediction by chaining of neural networks.
pubmed:affiliation
Katholieke Universiteit Leuven, Department of Electrical Engineering, ESAT-SISTA, Heverlee, Leuven, Belgium. duhoux@esat.kuleuven.ac.be
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't