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Active learning from process data
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DOI：10.1002/aic.690441009.png)
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摘要
En 中文
Much of the prevailing connectionist machine learning research in chemical engineering assumes a one-way passive relationship between the learner and the application domain. This article investigates a two-way active relationship between learner and domain. An active relationship is useful and even necessary if the prevailing research is to be successfully applied to real-world problems involving sparse and strongly biased data. A process development case study is used to illustrate the impact of data quality and quantity and to compare the performance of active learning against conventional passive learning. This study highlights on the need to assess data quality and demonstrates the improvements in the rate of active learning.
Keyword:
ARTIFICIAL NEURAL NETWORKS
FAULT
DIAGNOSIS
KNOWLEDGE
OPTIMIZATION
DESIGN
MODEL
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期刊
IF:
4
论文数:
1.1W
被引数:
2.9W
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