Data preprocessing and output evaluation of an autoassociative neural network model for online fault detection in virginiamycin production
Data preprocessing and output evaluation of an autoassociative neural network model for online fault detection in virginiamycin production
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DOI：
10.1263/jbb.94.70
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发表时间：
2002-07-01
影响因子：
2.8
通讯作者：
Shioya, S
中科院分区：
文献类型：
--
作者：
Huang, JH;Shimizu, H;Shioya, S
在这项研究中，使用人工自联想神经网络（AANN）在线检测使用传统工艺变量的正常抗生素生产发酵的偏差。为了提高提取多维过程变量中隐藏信息的效率，最终使AANN足以进行故障检测，我们探索了以下方法：过程变量的选择；数据预处理，涉及标准化 AANN 的训练数据；以及涉及评估 AANN 输出的数据评估。基于这些技术，成功开发了弗吉尼亚链霉菌生产弗吉尼亚霉素M和S的故障检测方法。
In this study, an artificial autoassociative neural network (AANN) was used online to detect deviations from normal antibiotic production fermentation using conventional process variables. To improve the efficiency of extracting hidden information contained in multidimensional process variables, and to finally render the AANN adequate for fault detection, we explored the following methods: selection of process variables; preprocessing of data that involved normalizing the training data of the AANN; and evaluation of data that involved assessing the output of the AANN. A method for fault detection in virginiamycin M and S production by Streptomyces virginiae was successfully developed based on these techniques.