Sequential adaptive networks: An ensemble of neural networks for feed forward control of L-methionine production

2009 ◽  
Vol 64 (10) ◽  
pp. 2401-2412 ◽  
Author(s):  
Rajib Nayak ◽  
James Gomes
Keyword(s):  
Neural Networks ◽  
Adaptive Networks ◽  
Feed Forward ◽  
Feed Forward Control

Flatness Feed-Forward Control Model Based on Neural Networks

2014 ◽  
Vol 989-994 ◽  
pp. 3386-3389
Author(s):  
Zhu Wen Yan ◽  
Hen An Bu ◽  
Dian Hua Zhang ◽  
Jie Sun
Keyword(s):  
Neural Networks ◽  
Rolling Mill ◽  
Rolling Force ◽  
Work Roll ◽  
Control Model ◽  
Good Effect ◽  
Feed Forward ◽  
Force Fluctuations ◽  
Feed Forward Control ◽  
Roll Bending

The influence on the shape of the strip from rolling force fluctuations has been analyzed. The combination of intermediate roll bending and work roll bending has been adopted. The principle of rolling force feed-forward control has been analyzed. The feed-forward control model has been established on the basis of neural networks. The model has been successfully applied to a rolling mill and a good effect has been achieved.

scholarly journals Model-based feed-forward control for time-varying systems with an example for SRF cavities

2020 ◽  
Vol 53 (2) ◽  
pp. 1331-1336
Author(s):  
Sven Pfeiffer ◽  
Annika Eichler ◽  
Holger Schlarb
Keyword(s):  
Time Varying ◽  
Feed Forward ◽  
Feed Forward Control ◽  
Model Based ◽  
Time Varying Systems ◽  
Srf Cavities

An Evolutionary Approach to the Use of Neural Networks in the Segmentation of Handwritten Numerals

1997 ◽  
Vol 11 (05) ◽  
pp. 717-734 ◽  
Author(s):  
J. M. Westall ◽  
M. S. Narasimha
Keyword(s):  
Neural Networks ◽  
Production System ◽  
Background Noise ◽  
Evolutionary Approach ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
Decision Functions

Neural networks are now widely and successfully used in the recognition of handwritten numerals. Despite their wide use in recognition, neural networks have not seen widespread use in segmentation. Segmentation can be extremely difficult in the presence of connected numerals, fragmented numerals, and background noise, and its failure is a principal cause of rejected and incorrectly read documents. Therefore, strategies leading to the successful application of neural technologies to segmentation are likely to yield important performance benefits. In this paper we identify problems that have impeded the use of neural networks in segmentation and describe an evolutionary approach to applying neural networks in segmentation. Our approach, based upon the use of monotonic fuzzy valued decision functions computed by feed-forward neural networks, has been successfully employed in a production system.

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