Predicting the internal bond strength of particleboard, utilizing a radial basis function neural network

Abstract Development of a model to identify process relationships and predict parameter values in a continuous manufacturing operation is often a difficult undertaking. Process parameters are typically dynamic, and are functions of complex relationships and interactions between process parameters. A radial basis function (RBF) neural network was used to develop a process model for predicting the strength of particleboard. The RBF algorithm was modified using a conscience function to ensure that the distribution of the data was described in each dimension. The trained network was successful at predicting the internal bond strength parameter with an average prediction error of 12.5%. This predictive capability is superior to other neural-network and statistical models developed to predict internal bond. Predictions of this accuracy would allow the trained network model to be used to improve process control in a particleboard manufacturing plant.

[1]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[2]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[3]  William E. Jones,et al.  Back-propagation, a generalized delta learning rule , 1987 .

[4]  James D. Keeler,et al.  Layered Neural Networks with Gaussian Hidden Units as Universal Approximations , 1990, Neural Computation.

[5]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[6]  Deborah F. Cook,et al.  Neural-network process modeling of a continuous manufacturing operation , 1993 .

[7]  Deborah F. Cook,et al.  Counterpropagation neural network for modelling a continuous correlated process , 1995 .

[8]  Simon Haykin,et al.  Radial basis function classification of impulse radar waveforms , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[9]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .

[10]  S. Renals,et al.  Phoneme classification experiments using radial basis functions , 1989, International 1989 Joint Conference on Neural Networks.

[11]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[12]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[13]  R.P. Lippmann,et al.  Pattern classification using neural networks , 1989, IEEE Communications Magazine.

[14]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[15]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[16]  Deborah F. Cook,et al.  A predictive neural network modelling system for manufacturing process parameters , 1992 .