Neural network procedures for experimental analysis with censored data

C. Su, C. Miao
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引用次数: 13

Abstract

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.
神经网络程序的实验分析与审查的数据
由于一些不可控的因素,一个实验只能完成一部分。这种不完整的数据通常称为删减数据。分析删减数据的传统方法在计算上是复杂的。本文提出了一种应用神经网络分析单截尾数据实验的有效方法。提出了两种比极大似然估计和田口分钟累积分析等传统方法更简单的方法。此外,还给出了三个数值算例,将所提方法与常规方法进行了比较。这些比较表明所建议的程序是有效和可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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