An ENA-based strategy replacing subobjectives definition in incremental learning

Incremental evolution has proved to be extremely useful in complex process control. The need to define manually the subobjectives at each stage constitutes the method weakest point, hindering its generalization. We propose the application of evolving neural arrays (ENA) in order to implement increme...

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Bibliographic Details
Published inProceedings of the 25th International Conference on Information Technology Interfaces, 2003. ITI 2003 pp. 383 - 390
Main Authors Corbalan, L., Lanzarini, L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2003
Subjects
Artificial neural networks
Biological neural networks
Brain modeling
Character recognition
Circuit simulation
Computer science
Genetic algorithms
Process control
Proposals
Throughput
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Summary:Incremental evolution has proved to be extremely useful in complex process control. The need to define manually the subobjectives at each stage constitutes the method weakest point, hindering its generalization. We propose the application of evolving neural arrays (ENA) in order to implement incremental evolution (without the explicit explanation of subobjectives) applicable to a large set of process control problems. The measures carried out show the advantage of evolving neural arrays over traditional methods handling neural network populations. SANE has been particularly used as comparing reference for its high throughput.
ISBN:9789539676962
9539676967
ISSN:1330-1012
DOI:10.1109/ITI.2003.1225374