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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 patterns, 2 shift patterns, one trend pattern, and one cyclic pattern in addition to the latest decision point.
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fig15: Effect of threshold on the average run length as a function of the standardized shift size for an ensemble that includes 3 normal patterns, 2 shift patterns, one trend pattern, and one cyclic pattern in addition to the latest decision point.

Mentions: Ensemble 1 is formed of 3 ANNs devoted to discovering normal patterns, 2 ANNs for shift patterns, one ANN for trend patterns, and one ANN for cyclic patterns. The last decision point is added as an input as well as the output of the selected ANNs to the leader net. If the output of the leader net is greater than an assigned threshold, the pattern is classified as a shift pattern and the process is stopped. The ARL is then the number of prior decision points. The results obtained from the ensemble are shown in Figure 15. The ARL increases as you select a higher threshold. In general, the performance of this suggested ensemble is not as good as the X-bar chart. For example, the length of the ARL at zero shift size (normal) is shorter than that of the benchmark X-bar 3 sigma which is 373.


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 patterns, 2 shift patterns, one trend pattern, and one cyclic pattern in addition to the latest decision point.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig15: Effect of threshold on the average run length as a function of the standardized shift size for an ensemble that includes 3 normal patterns, 2 shift patterns, one trend pattern, and one cyclic pattern in addition to the latest decision point.
Mentions: Ensemble 1 is formed of 3 ANNs devoted to discovering normal patterns, 2 ANNs for shift patterns, one ANN for trend patterns, and one ANN for cyclic patterns. The last decision point is added as an input as well as the output of the selected ANNs to the leader net. If the output of the leader net is greater than an assigned threshold, the pattern is classified as a shift pattern and the process is stopped. The ARL is then the number of prior decision points. The results obtained from the ensemble are shown in Figure 15. The ARL increases as you select a higher threshold. In general, the performance of this suggested ensemble is not as good as the X-bar chart. For example, the length of the ARL at zero shift size (normal) is shorter than that of the benchmark X-bar 3 sigma which is 373.

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.