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An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts.

Barghash M - Comput Intell Neurosci (2015)

Bottom Line: Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble.In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity.It also can be used to discover even small shifts in the mean as early as possible.

View Article: PubMed Central - PubMed

Affiliation: IE Department, The University of Jordan, Amman 11942, Jordan.

ABSTRACT
Pattern recognition in control charts is critical to make a balance between discovering faults as early as possible and reducing the number of false alarms. This work is devoted to designing a multistage neural network ensemble that achieves this balance which reduces rework and scrape without reducing productivity. The ensemble under focus is composed of a series of neural network stages and a series of decision points. Initially, this work compared using multidecision points and single-decision point on the performance of the ANN which showed that multidecision points are highly preferable to single-decision points. This work also tested the effect of population percentages on the ANN and used this to optimize the ANN's performance. Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble. The ensemble that used only optimized ANNs has improved performance over individual ANNs and three-sigma level rule. In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity. It also can be used to discover even small shifts in the mean as early as possible.

No MeSH data available.


Effect of threshold on the average run length as a function of the standardized shift size for a neural network trained with 70% shift, 30% normal population.
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Related In: Results  -  Collection


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fig12: Effect of threshold on the average run length as a function of the standardized shift size for a neural network trained with 70% shift, 30% normal population.

Mentions: The effect of population percentages is analyzed according to Figure 11 where the ANNs are trained in accordance with different population percentages tested for the ARL. Figures 12 and 13 show the results of a nonoptimized 30% normal and 70% shift pattern and the optimized ANN trained with 75% normal and 25% shift pattern. The nonoptimized ANN ARL is worse than that of the benchmark X-bar chart with three-sigma level rule. For the case of optimised ANN, the ARL for the case zero shift is extremely high while reducing the ARL for the case of standard 0.5 shift level to less than 150.


An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts.

Barghash M - Comput Intell Neurosci (2015)

Effect of threshold on the average run length as a function of the standardized shift size for a neural network trained with 70% shift, 30% normal population.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4538969&req=5

fig12: Effect of threshold on the average run length as a function of the standardized shift size for a neural network trained with 70% shift, 30% normal population.
Mentions: The effect of population percentages is analyzed according to Figure 11 where the ANNs are trained in accordance with different population percentages tested for the ARL. Figures 12 and 13 show the results of a nonoptimized 30% normal and 70% shift pattern and the optimized ANN trained with 75% normal and 25% shift pattern. The nonoptimized ANN ARL is worse than that of the benchmark X-bar chart with three-sigma level rule. For the case of optimised ANN, the ARL for the case zero shift is extremely high while reducing the ARL for the case of standard 0.5 shift level to less than 150.

Bottom Line: Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble.In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity.It also can be used to discover even small shifts in the mean as early as possible.

View Article: PubMed Central - PubMed

Affiliation: IE Department, The University of Jordan, Amman 11942, Jordan.

ABSTRACT
Pattern recognition in control charts is critical to make a balance between discovering faults as early as possible and reducing the number of false alarms. This work is devoted to designing a multistage neural network ensemble that achieves this balance which reduces rework and scrape without reducing productivity. The ensemble under focus is composed of a series of neural network stages and a series of decision points. Initially, this work compared using multidecision points and single-decision point on the performance of the ANN which showed that multidecision points are highly preferable to single-decision points. This work also tested the effect of population percentages on the ANN and used this to optimize the ANN's performance. Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble. The ensemble that used only optimized ANNs has improved performance over individual ANNs and three-sigma level rule. In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity. It also can be used to discover even small shifts in the mean as early as possible.

No MeSH data available.