Distinguishing between mean, variance and autocorrelation changes in statistical quality control
提出一种多阶段决策树方法，结合神经网络和二次判别函数，用于区分质量信号中的均值、方差和自相关变化，实验成功率达84%。
In order to enhance the probability of correct quality diagnosis, it is useful to be able to identify the statistical manner in which the quality signal has changed, i.e. identify change structure. Specifically we wish to distinguish between changes in mean, variance and lag one autocorrelation. Because these change structures yield significant similarities in their corresponding output, a multistage decision tree is necessary. A multistage classification system with a neural network and quadratic discriminant functions is used, where neural network output is an a priori distribution for the Bayesian quadratic discriminant function. Experimental results show that this multistage decision strategy performs significantly better than its single stage counterpart, with an overall success rate of 84%.