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Using radial basis function neural networks to recognize shifts in correlated manufacturing process parameters

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References

1977Time Series Analysis: Forecasting and Control19,200 cited
1977Time Series Analysis: Forecasting and Control6,446 cited
1993Forecasting methods and applications2,393 cited
1988Time-Series Modeling for Statistical Process Control724 cited
1984Forecasting: Methods and Applications636 cited
1991Statistical process control procedures for correlated observations382 cited
1992Control Charts in the Presence of Data Correlation178 cited
1994X -bar and R control chart interpretation using neural computing133 cited
1993X¯ CONTROL CHART PATTERN IDENTIFICATION THROUGH EFFICIENT OFF-LINE NEURAL NETWORK TRAINING104 cited
1990Comparing Scientific Word Processor Packages: T3 and Vuwriter101 cited
1991A comparison of neural networks to SPC charts85 cited

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