Abstract
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 the predictive capability of the network. The convergence criteria were tightened, the number of hidden nodes and hidden layers was increased. These modifications did not improve the predictive capability of the composite board networks.
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References
Baffes, P. T. (1989)NETS User's Guide. Software Technology Branch, Lyndon B. Johnson Space Center, Clear Lake, Texas.
Bauer, K. B. (1988) Parallel distributed processing models using the back-propagation rule for studying analytic and holistic modes of processing in category learning. Master's Thesis, Texas A & M University, Texas.
Caudill, M. (1989)Neural Networks Primer. Miller Freeman Publications, California.
Cook, D. F. (1990) A predictive modeling system for manufacturing process parameters, PhD Dissertation, Texas A & M University, Texas.
Hinton, G. E. (1986) Learning in massively parallel nets, inProceedings of the International Conference on Artificial Intelligence.
Hornik, K., Stinchcombe M. and White, H. (1990) Universal approximation of an unknown mapping and its derivative using multilayer feedforward networks.Discussion Paper 89-36R. University of California, California.
Hoskins, J. C. and Himmelblau, D. M. (1988) Artificial neural network models of knowledge representation in chemical engineering.Computers and Chemical Engineering 12, 881–90.
Huang, W. Y. and Lippmann, R. P. (1987)IEEE International Conference on Neural Networks, 485–92.
Jones, W. P. and Hoskins, J. (1987) Back-propagation: a generalized delta learning rule.Byte,12, 155–62.
Lippmann, R. P. (1987) An introduction to computing with neural nets.IEEE ASSP Magazine 4, 4–22.
Pao, Y. H. (1989)Adaptive Pattern Recognition and Neural Networks. Addison-Wesley Publishing Company Inc. Mass.
Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) Learning internal representations by error propagation, inParallel Distributed Processing: Exploration in the Microstructures of Cognition, Vol. 1: Foundations, Rumelhart, D. E. and McClelland, J. L. (eds), MIT Press, Mass.
Sejnowski, T. and Rosenberg, C. R. (1986) NETtalk: a parallel network that learns to read aloud.Johns Hopkins University Technical Report JHU/EECS-86/01.
White, H. H. (1989) Neural network learning and statistics.AI Expert 4, 48–52.
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Cook, D.F., Shannon, R.E. A sensitivity analysis of a back-propagation neural network for manufacturing process parameters. J Intell Manuf 2, 155–163 (1991). https://doi.org/10.1007/BF01471362
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DOI: https://doi.org/10.1007/BF01471362