Inferential estimation of polymer quality using bootstrap aggregated neural networks
- Author
- ZHANG, J1
[1] Centre for Process Analytics and Control Technology, Department of Chemical and Process Engineering, University of Newcastle, Newcastle upon Tyne NE1 7RU, United Kingdom - Source
Neural networks. 1999, Vol 12, Num 6, pp 927-938 ; ref : 41 ref
- ISSN
- 0893-6080
- Scientific domain
- Electronics; Computer science; Neurology
- Publisher
- Elsevier Science, Oxford
- Publication country
- United Kingdom
- Document type
- Article
- Language
- English
- Keyword (fr)
- Analyse composante principale Estimation Etude théorique Généralisation Intervalle confiance Robustesse Règle inférence Réacteur polymérisation Réseau neuronal
- Keyword (en)
- Principal component analysis Estimation Theoretical study Generalization Confidence interval Robustness Inference rule Polymerization reactor Neural network
- Keyword (es)
- Análisis componente principal Estimación Estudio teórico Generalización Intervalo confianza Robustez Regla inferencia Reactor polimerización Red neuronal
- Classification
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D02 Computer science; control theory; systems / 001D02C Artificial intelligence / 001D02C06 Connectionism. Neural networks
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D02 Computer science; control theory; systems / 001D02D Control theory. Systems / 001D02D09 Process control. Computer integrated manufacturing
- Discipline
- Computer science : theoretical automation and systems
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 1898347
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
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DOI
10.1016/S0893-6080(99)00037-4 
https://dx.doi.org/10.1016/S0893-6080(99)00037-4
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