A Novel Firing Rule For Training Kohonen SelforganisingMaps

Abstract

Statistical Process Control Charts can exhibit six principal types of patterns: Normal, Cyclic, Increasing Trend, Decreasing Trend, Upward Shift and Downward Shift. All except Normal patterns indicate abnormalities in the process that must be corrected. Accurate and speedy detection of such patterns is important to achieving tight control of the process and ensuring good product quality. This paper describes an implementation of the Kohonen self-organising map which employs the Euclidean Distance as the firing rule for control chart pattern recognition. First, the structure of the network is outlined and the equations which govern its dynamics are given. Then the learning mechanism of the network is explained. The effects of different combinations of network parameters on classificatio n accuracy are discussed. A novel firing rule for the Kohonen self-organising map is proposed. This rule involves component-bycomponent comparison between the input pattern and the established class templates. When an input vector is presented, it is compared with the class templates in all the neurons in turn. The neuron containing the class template that best matches the input vector will subsequently fire. This approach is intended to enhance the generalisation capability and accuracy of the Kohonen selforganising map. The paper gives a comparison of the results obtained using the Euclidean Distance and the proposed firing rule.