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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.


Population formation based on the shift pattern and normal pattern percentages.
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Related In: Results  -  Collection


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fig7: Population formation based on the shift pattern and normal pattern percentages.

Mentions: Preparation of an individual neural network based on the work of Barghash and Santarisi [6] is done by generating shift patterns and normal patterns as shown in Figure 7. A randomized selector is used to perform the pattern selection to construct a population with preset pattern type percentages.


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

Barghash M - Comput Intell Neurosci (2015)

Population formation based on the shift pattern and normal pattern percentages.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig7: Population formation based on the shift pattern and normal pattern percentages.
Mentions: Preparation of an individual neural network based on the work of Barghash and Santarisi [6] is done by generating shift patterns and normal patterns as shown in Figure 7. A randomized selector is used to perform the pattern selection to construct a population with preset pattern type percentages.

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.