Combining a neural network with a genetic algorithm for process parameter optimization

A neural-network model has been developed to predict the value of a critical strength parameter (internal bond) in a particleboard manufacturing process, based on process operating parameters and conditions. A genetic algorithm was then applied to the trained neural network model to determine the pr...

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Bibliographic Details
Published in:Engineering applications of artificial intelligence 2000-08, Vol.13 (4), p.391-396
Main Authors: Cook, D.F., Ragsdale, C.T., Major, R.L.
Format: Article
Language:English
Subjects:
Genetic algorithm
Neural-network
Process optimization
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Summary:A neural-network model has been developed to predict the value of a critical strength parameter (internal bond) in a particleboard manufacturing process, based on process operating parameters and conditions. A genetic algorithm was then applied to the trained neural network model to determine the process parameter values that would result in desired levels of the strength parameter for given operating conditions. The integrated NN–GA system was successful in determining the process parameter values needed under different conditions, and at various stages in the process, to provide the desired level of internal bond. The NN–GA tool allows a manufacturer to quickly determine the values of critical process parameters needed to achieve acceptable levels of board strength, based on current operating conditions and the stage of manufacturing.
ISSN:0952-1976
1873-6769
DOI:10.1016/S0952-1976(00)00021-X