DocumentCode :
3595651
Title :
Neural networks for process control in steel manufacturing
Author :
Schlag, M. ; Broese, Einar ; Feldkeller, Bj?¶rn ; Granckow, O. ; Jansen, Michael ; Pappe, T. ; Schaffner, Christian ; S?¶rgel, G?¼nter
Author_Institution :
Corp. Tech. Dept., Siemens AG, Munich, Germany
Volume :
1
fYear :
1997
Firstpage :
155
Abstract :
Neural networks are particularly suitable for the approximation of non-linear time-variant functions. Due to their learning capabilities, they have proven useful in control applications for complex industrial processes. In collaboration with the Corporate Research and Development Department, the Siemens Industrial and Building Systems Group developed neural network applications for the steel industry, resulting in a more economic use of resources and an improvement of productivity. At this time Siemens has installed more than 100 neural nets world wide at various plants
Keywords :
electric furnaces; learning (artificial intelligence); neurocontrollers; process control; rolling mills; spatial variables control; steel industry; Corporate Research and Development Department; Siemens Industrial and Building Systems Group; complex industrial processes; learning capabilities; nonlinear time-variant functions approximation; process control; productivity; steel manufacturing; Collaboration; Electrical equipment industry; Industrial control; Manufacturing industries; Manufacturing processes; Metals industry; Neural networks; Process control; Research and development; Steel;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
0-8186-7919-0
Type :
conf
DOI :
10.1109/ICASSP.1997.599582
Filename :
599582
Link To Document :
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