Pattern recognition is an important issue in statistical process control (SPC) because unnatural patterns exhibited by control charts can be associated with specific assignable causes adversely affecting the process. Artificial neural networks have been widely investigated as an effective approach to control chart pattern (CCP) recognition in recent years. However, an overwhelming majority of these applications has used trial-and-error experiments to determine the network architecture and training parameters, which are crucial to the performance of the network. In this paper, the genetic algorithm (GA) is used to evolve the configuration and the training parameter set of the neural network to solve the online CCP recognition problem. Numerical results are provided that indicate that the proposed GA can evolve neural network architecture while simultaneously determining training parameters to maximize efficiently the performance of the online CCP recognizers. Because the population size is a major parameter of GA processing speed, an investigation was also conducted to identify the effects of the population size on the performance of the proposed GA. This research further confirms the feasibility of using GA to evolve neural networks. Although a back-propagation-based CCP recognizer is the particular application presented here, the proposed GA methodology can be applied to neural networks in general.
Unsupervised adaptive resonance theory neural networks for control chart pattern recognition
