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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.


Stabilized ANN through two decision points on the average run length as a function of the standardized shift size for a neural network trained with 25% shift, 75% normal population.
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Related In: Results  -  Collection


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fig10: Stabilized ANN through two decision points on the average run length as a function of the standardized shift size for a neural network trained with 25% shift, 75% normal population.

Mentions: Neural networks suffer from instabilities when using individual decisions. This instability can be defined as giving an erroneous decision temporarily and then giving the right decision back. This temporary misjudgment by the neural network reduces the ARL appreciably and deteriorates the performance of the neural network. This is solved by adding one or more decision points. If the ANN still insists on its decision in two or more following decisions points, this can be a sign on the existence of a nonnormal behavior. Multidecision point stabilization is tested in accordance with Figure 8, where the case of a single decision and 2 consecutive decision points are tested and the results for these two cases are shown in Figures 9 and 10, respectively. The performance of both ANNs is tested at triggering threshold levels 0.1–0.9. Two clear conclusions can be drawn; firstly, the threshold is a tuning parameter for the performance of the ANN.


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

Barghash M - Comput Intell Neurosci (2015)

Stabilized ANN through two decision points on the average run length as a function of the standardized shift size for a neural network trained with 25% shift, 75% normal population.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig10: Stabilized ANN through two decision points on the average run length as a function of the standardized shift size for a neural network trained with 25% shift, 75% normal population.
Mentions: Neural networks suffer from instabilities when using individual decisions. This instability can be defined as giving an erroneous decision temporarily and then giving the right decision back. This temporary misjudgment by the neural network reduces the ARL appreciably and deteriorates the performance of the neural network. This is solved by adding one or more decision points. If the ANN still insists on its decision in two or more following decisions points, this can be a sign on the existence of a nonnormal behavior. Multidecision point stabilization is tested in accordance with Figure 8, where the case of a single decision and 2 consecutive decision points are tested and the results for these two cases are shown in Figures 9 and 10, respectively. The performance of both ANNs is tested at triggering threshold levels 0.1–0.9. Two clear conclusions can be drawn; firstly, the threshold is a tuning parameter for the performance of the ANN.

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.