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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 an ensemble that includes 3 normal detecting patterns, 3 shift detecting patterns, 1 cyclic pattern, and 1 trend pattern.
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fig16: Effect of threshold on the average run length as a function of the standardized shift size for an ensemble that includes 3 normal detecting patterns, 3 shift detecting patterns, 1 cyclic pattern, and 1 trend pattern.

Mentions: Figure 16 shows the average run length versus standard shift size for an ensemble with 3 ANN normal detecting patterns, 3 ANN shift detecting patterns, 1 ANN detecting cyclic pattern, and 1 ANN detecting trend pattern. The results of this ensemble are slightly better than those for the X-bar chart 3-sigma level for the case of threshold = 0.9. The ARL for the case of normal patter (zero shift size) is nearly the same as that of the X-bar chart but the ARL at a shift size of 0.5 is lower than that of the X-bar chart. Thus this ensemble has lower type I and II error.


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 an ensemble that includes 3 normal detecting patterns, 3 shift detecting patterns, 1 cyclic pattern, and 1 trend pattern.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig16: Effect of threshold on the average run length as a function of the standardized shift size for an ensemble that includes 3 normal detecting patterns, 3 shift detecting patterns, 1 cyclic pattern, and 1 trend pattern.
Mentions: Figure 16 shows the average run length versus standard shift size for an ensemble with 3 ANN normal detecting patterns, 3 ANN shift detecting patterns, 1 ANN detecting cyclic pattern, and 1 ANN detecting trend pattern. The results of this ensemble are slightly better than those for the X-bar chart 3-sigma level for the case of threshold = 0.9. The ARL for the case of normal patter (zero shift size) is nearly the same as that of the X-bar chart but the ARL at a shift size of 0.5 is lower than that of the X-bar chart. Thus this ensemble has lower type I and II error.

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.