CONTROL CHART PATTERN-RECOGNITION USING A NEW-TYPE OF SELF-ORGANIZINGNEURAL-NETWORK

Control charts as used in statistical process control can exhibit six principal types of patterns: normal, cyclic, increasing trend, decreas ing trend, upward shift and downward shift. Apart from normal patterns , all the other patterns indicate abnormalities in the process that mu st be corrected. Accurate and speedy detection of such patterns is imp ortant to achieving tight control of the process and ensuring good pro duct quality. This paper describes a new type of neural network for co ntrol chart pattern recognition. The neural network is self-organizing and can learn to recognize new patterns in an on-line incremental man ner. The key feature of the proposed neural network is the criterion e mployed to select the firing neuron, i.e. the neuron indicating the pa ttern class. The paper gives a comparison of the results obtained usin g the proposed network and those for other self-organizing networks em ploying a different firing criterion.