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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.


Suggested ensemble construction.
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fig14: Suggested ensemble construction.

Mentions: The basic shape of the generalized suggested ensemble is shown in Figure 14. The first stage is a selected neural network from four main categories: normal pattern detectors, shift pattern detectors, trend pattern detectors, and cyclic pattern detectors. Then the last two decisions of each neural network are passed to the leader net which is trained to give the decision of whether the pattern is normal or not. A question may arise on the reason for considering patterns other than normal and shift patterns in this work, although only shifts are intended to be discovered. The reason has two sides; the first relates to the fact that these patterns are present in real life and in other research works. Thus, it is imperative that the suggested ensembles be designed to discover and classify all patterns, although this work reports only on its ability to discover shift patterns. The second side relates to the problem that the ability to discover shift patterns might be affected if more patterns are included; thus it seems more logical to include these in any realistic attempt to recognize shift patterns in control charts.


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

Barghash M - Comput Intell Neurosci (2015)

Suggested ensemble construction.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig14: Suggested ensemble construction.
Mentions: The basic shape of the generalized suggested ensemble is shown in Figure 14. The first stage is a selected neural network from four main categories: normal pattern detectors, shift pattern detectors, trend pattern detectors, and cyclic pattern detectors. Then the last two decisions of each neural network are passed to the leader net which is trained to give the decision of whether the pattern is normal or not. A question may arise on the reason for considering patterns other than normal and shift patterns in this work, although only shifts are intended to be discovered. The reason has two sides; the first relates to the fact that these patterns are present in real life and in other research works. Thus, it is imperative that the suggested ensembles be designed to discover and classify all patterns, although this work reports only on its ability to discover shift patterns. The second side relates to the problem that the ability to discover shift patterns might be affected if more patterns are included; thus it seems more logical to include these in any realistic attempt to recognize shift patterns in control charts.

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.