A sensitivity analysis of a back-propagation neural network for manufacturing process parameters
@article{Cook1991ASA, title={A sensitivity analysis of a back-propagation neural network for manufacturing process parameters}, author={D. F. Cook and R. E. Shannon}, journal={Journal of Intelligent Manufacturing}, year={1991}, volume={2}, pages={155-163} }
Back-propagation neural networks that represent specific process parameters in a composite board manufacturing process were analyzed to determine their sensitivity to network design and to the values of the learning parameters used in the back-propagation algorithm. The effects of the number of hidden layers, the number of nodes in a hidden layer, and the values of the learning rate and momentum factor were studied. Three network modification strategies were applied to evaluate their effect on… Expand
Topics from this paper
12 Citations
Initial analysis on sensitivity of multilayer perceptron
- Computer Science
- IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028)
- 1999
- 5
A new approach for manufacturing forecast problems with insufficient data: the case of TFT–LCDs
- Computer Science
- J. Intell. Manuf.
- 2013
- 13
A new virtual-sample-generating method based on the heuristics algorithm
- Computer Science
- Proceedings of 2013 IEEE International Conference on Grey systems and Intelligent Services (GSIS)
- 2013
- 2
- Highly Influenced
Sensitivity analysis for feedforward artificial neural networks with differentiable activation functions
- Computer Science
- [Proceedings 1992] IJCNN International Joint Conference on Neural Networks
- 1992
- 109
A genetic algorithm-based virtual sample generation technique to improve small data set learning
- Computer Science
- Neurocomputing
- 2014
- 41
- Highly Influenced
Using past manufacturing experience to assist building the yield forecast model for new manufacturing processes
- Engineering, Computer Science
- J. Intell. Manuf.
- 2012
- 8
- Highly Influenced
References
SHOWING 1-10 OF 11 REFERENCES
Artificial neural network models for knowledge representation in chemical engineering
- Computer Science
- 1990
- 343
Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
- Mathematics, Computer Science
- Neural Networks
- 1990
- 1,646
- PDF
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
- Computer Science
- 1986
- 15,352
- PDF
Learning internal representations by error propagation
- Computer Science, Mathematics
- 1986
- 18,438
- Highly Influential
- PDF