DocumentCode :
2177505
Title :
Subsystem modelling using neural networks with application to manufacturing systems
Author :
Cooper, P.L. ; Savage, G.J.
Author_Institution :
Dept. of Syst. Design Eng., Waterloo Univ., Ont., Canada
Volume :
3
fYear :
1998
fDate :
11-14 Oct 1998
Firstpage :
3055
Abstract :
Where traditional methods of robust design have their limitations in manufacturing systems, it appears that system modelling can provide advantages. When manufacturing systems can be split into subsystems, subsystem models and continuity and compatibility constraints are required to develop an overall system model. Where physical information is lacking, empirical models can be created. Herein, the radial basis function neural network method for empirical modelling is discussed and procedures for connecting these models with physical system information is demonstrated using a cooling fin problem
Keywords :
interpolation; modelling; production control; radial basis function networks; cooling fin problem; interpolation; manufacturing systems; production control; radial basis function neural network; subsystem modelling; Cooling; Costs; Design engineering; Design methodology; Joining processes; Manufacturing processes; Manufacturing systems; Neural networks; Robustness; Systems engineering and theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on
Conference_Location :
San Diego, CA
ISSN :
1062-922X
Print_ISBN :
0-7803-4778-1
Type :
conf
DOI :
10.1109/ICSMC.1998.725130
Filename :
725130
Link To Document :
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