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首页> 外文期刊>Expert Systems with Application >An Elman neural network-based model for predicting anti-germ performances and ingredient levels with limited experimental data
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An Elman neural network-based model for predicting anti-germ performances and ingredient levels with limited experimental data

机译：基于Elman神经网络的模型，用于以有限的实验数据预测抗细菌繁殖性能和成分水平

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摘要

Anti-germ performance test is critical in the production of detergents. However, actual biochemical tests are often costly and time-consuming. In this paper, we present an Elman neural network-based model to predict detergents' anti-germ performance and ingredient levels, respectively. The model made it much faster and cost effective than doing actual biochemical tests. We also present preprocessing methods that can reduce data conflicts while keeping the monotonicity on limited experimental data. The model can find out the relationship between ingredient levels and the corresponding anti-germ performance, which is not widely used in solving biochemical problems. The results of experiments are generated on the base of two detergent products for two types of bacteria, and appear reasonable according to natural rules. The prediction results show a high accuracy and fitting with the monotonicity rule mostly.
机译：抗细菌性能测试对于洗涤剂的生产至关重要。然而，实际的生化测试通常是昂贵且费时的。在本文中，我们提出了一个基于Elman神经网络的模型来分别预测洗涤剂的抗细菌性能和成分水平。与进行实际的生化测试相比，该模型使其更快，更经济。我们还提出了预处理方法，可以减少数据冲突，同时在有限的实验数据上保持单调性。该模型可以找出成分水平与相应的抗细菌性能之间的关系，该关系并未广泛用于解决生化问题。实验结果是在两种洗涤剂的两种洗涤剂产品的基础上得出的，并且根据自然规律显得合理。预测结果表明，该方法具有较高的准确性，并且大多符合单调性规则。

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  • 来源
    《Expert Systems with Application》 |2011年第7期|p.8186-8192|共7页
  • 作者

    Anqi Cui; Hua Xu; Peifa Jia;

  • 作者单位

    State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;

    State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;

    State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    anti-germ performance prediction; ingredient level prediction; artificial neural networks; monotonicity rule; preprocessing methods;

    机译：反细菌性能预测;成分水平预测;人工神经网络;单调性规则预处理方法;

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