TY - JOUR
T1 - Spiking neural network-based control chart pattern recognition
AU - Awadalla, Medhat H.A.
AU - Abdellatif Sadek, M.
PY - 2012/3
Y1 - 2012/3
N2 - Due to an increasing competition in products, consumers have become more critical in choosing products. The quality of products has become more important. Statistical Process Control (SPC) is usually used to improve the quality of products. Control charting plays the most important role in SPC. Control charts help to monitor the behavior of the process to determine whether it is stable or not. Unnatural patterns in control charts mean that there are some unnatural causes for variations in SPC. Spiking neural networks (SNNs) are the third generation of artificial neural networks that consider time as an important feature for information representation and processing. In this paper, a spiking neural network architecture is proposed to be used for control charts pattern recognition (CCPR). Furthermore, enhancements to the SpikeProp learning algorithm are proposed. These enhancements provide additional learning rules for the synaptic delays, time constants and for the neurons thresholds. Simulated experiments have been conducted and the achieved results show a remarkable improvement in the overall performance compared with artificial neural networks.
AB - Due to an increasing competition in products, consumers have become more critical in choosing products. The quality of products has become more important. Statistical Process Control (SPC) is usually used to improve the quality of products. Control charting plays the most important role in SPC. Control charts help to monitor the behavior of the process to determine whether it is stable or not. Unnatural patterns in control charts mean that there are some unnatural causes for variations in SPC. Spiking neural networks (SNNs) are the third generation of artificial neural networks that consider time as an important feature for information representation and processing. In this paper, a spiking neural network architecture is proposed to be used for control charts pattern recognition (CCPR). Furthermore, enhancements to the SpikeProp learning algorithm are proposed. These enhancements provide additional learning rules for the synaptic delays, time constants and for the neurons thresholds. Simulated experiments have been conducted and the achieved results show a remarkable improvement in the overall performance compared with artificial neural networks.
KW - Control chart pattern recognition
KW - Spikeprop algorithm
KW - Spiking neural network
UR - http://www.scopus.com/inward/record.url?scp=84866731264&partnerID=8YFLogxK
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U2 - 10.1016/j.aej.2012.07.004
DO - 10.1016/j.aej.2012.07.004
M3 - Article
AN - SCOPUS:84866731264
SN - 1110-0168
VL - 51
SP - 27
EP - 35
JO - Alexandria Engineering Journal
JF - Alexandria Engineering Journal
IS - 1
ER -