Boltzmann machines that learn to recognize patterns on control charts

Boltzmann machines (BM), a type of neural networking algorithm, have been proven to be useful in pattern recognition. Patterns on quality control charts have long been recognized as providing useful information for correcting process performance problems. In computer-integrated manufacturing environments, where the control charts are monitored by computer algorithms, the potential for using pattern-recognition algorithms is considerable. The main purpose of this paper is to formulate a Boltzmann machine pattern recognizer (BMPR) and demonstrate its utility in control chart pattern recognition. It is not the intent of this paper to make comparisons between existing related algorithms. A factorial design of experiments was conducted to study the effects of numerous factors on the convergence behavior and performance of these BMPRs. These factors include the number of hidden nodes used in the network and the annealing schedule. Simulations indicate that the temperature level of the annealing schedule significantly affects the convergence behavior of the training process and that, to achieve a balanced performance of these BMPRs, a medium to high level of annealing temperatures is recommended. Numerical results for cyclical and stratification patterns illustrate that the classification capability of these BMPRs is quite powerful.