Control Chart Pattern Recognition Using Spiking Neural Networks

Publisher Summary Statistical process control (SPC) is a method for improving the quality of products. Control charting plays the most important role in SPC. A control chart can be used to indicate whether a manufacturing process is under control. Unnatural patterns in control charts mean that there are some unnatural causes for variations. Therefore, control chart pattern recognition is important in SPC. In recent years, neural network techniques have increasingly been applied to pattern recognition. Spiking neural networks (SNNs) are the third generation of artificial neural networks, with spiking neurons as processing elements. In SNNs, time is an important feature for information representation and processing. Latest research has shown SNNs to be computationally more powerful than other types of artificial neural networks. This chapter proposes the application of SNN techniques to control chart pattern recognition. It focuses on the architecture and the learning procedure of the network. Experimental studies are illustrated to prove that the proposed architecture and the learning procedure give high pattern recognition accuracies.

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