期刊
ENGINEERING APPLICATIONS OF NEURAL NETWORKSX
卷 1000, 期 -, 页码 66-79出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-20257-6_6
关键词
Echo-State Neural Networks; Modelling; Off-gas management; Energy management; Steel industry
类别
资金
- Research Fund for Coal and Steel of the European Union [RFSR-CT-2015-00029]
Echo-State Neural Networks represent a very efficient solution for modelling of dynamic systems, thanks to their particular structure, which allows faithful reproduction of the behavior of the system to model with a usually limited computational burden for a training phase. This aspect favors the deployment of Echo-State Neural networks in the industrial field. In this paper, a novel application of such approach is proposed for the modelling of industrial processes. The developed models are part of a complex system for optimizing the exploitation of process off-gases in an integrated steelwork. Two models are presented and discussed, where both shallow Echo-State Neural Networks and Deep Echo State Neural networks are applied. The achieved results are presented and discussed, by comparing advantages and drawbacks of both approaches.
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