• Publications
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Combining a neural network with a genetic algorithm for process parameter optimization
TLDR
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. Expand
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Predicting the internal bond strength of particleboard, utilizing a radial basis function neural network
TLDR
A radial basis function neural network was used to develop a process model for predicting the strength of particleboard. Expand
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Using radial basis function neural networks to recognize shifts in correlated manufacturing process parameters
Traditional statistical process control (SPC) techniues of control charting are not applicable in many process industries because data from these facilities are autocorrelated. Therefore theExpand
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Utilization of neural networks for the recognition of variance shifts in correlated manufacturing process parameters
Traditional statistical process control (SPC) charting techniques were developed for use in discrete industries where independence exists between process parameters over time. Process parameters fromExpand
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A sensitivity analysis of a back-propagation neural network for manufacturing process parameters
TLDR
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. Expand
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Multivariate measurement system analysis in multisite testing: An online technique using principal component analysis
TLDR
We propose an online multivariate MSA approach to detecting faulty test instruments in a multisite testing system to assess its testing capability. Expand
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Exploring patient perceptions of healthcare service quality through analysis of unstructured feedback
TLDR
We apply a text mining methodology to a large set of textual feedback of physicians by their patients and relate the textual commentary to their numeric ratings. Expand
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Evaluation of neural network variable influence measures for process control
TLDR
A comprehensive approach to characterizing variable influence within a trained neural network model that can be used to guide decision makers in dynamic process control. Expand
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Design of a radial basis function neural network with a radius-modification algorithm using response surface methodology
TLDR
A radial basis function (RBF) neural network was designed for time series forecasting using both an adaptive learning algorithm and response surface methodology (RSM). Expand
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A predictive neural network modelling system for manufacturing process parameters
A methodology to predict the occurrence of out-of-control process conditions in a composite board manufacturing facility was developed using neural network theory. Multi-variable regression and timeExpand
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