Application of Spiking Neural Networks and the Bees Algorithm to Control Chart Pattern Recognition

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. Control chart pattern recognition is therefore 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 PhD Thesis proposes the application of SNN techniques to control chart pattern recognition. The thesis work focuses on the architecture and the learning procedure of the network. Experiments show that the proposed architecture and the learning procedure give high pattern recognition accuracies.