Neural network procedures for experimental analysis with censored data

Owing to some uncontrollable factors, only a portion of an experiment can be completed. Such incomplete data are generally referred to as censored data. Conventional approaches for analysis of censored data are computationally complicated. In this work an effective means of applying neural networks to analyze an experiment with singly‐censored data is presented. Two procedures are developed, which are simpler than conventional ones such as maximum likelihood estimation and Taguchi’s minute accumulating analysis. In addition, three numerical examples are presented to compare the proposed procedures with the conventional ones. Those comparisons reveal that proposed procedures are effective and feasible.