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  • ISSN 1006-3080
  • CN 31-1691/TQ

一种改进的SOM神经网络在污水处理故障诊断中的应用

岳宇飞 罗健旭

岳宇飞, 罗健旭. 一种改进的SOM神经网络在污水处理故障诊断中的应用[J]. 华东理工大学学报(自然科学版), 2017, (3): 389-396. doi: 10.14135/j.cnki.1006-3080.2017.03.015
引用本文: 岳宇飞, 罗健旭. 一种改进的SOM神经网络在污水处理故障诊断中的应用[J]. 华东理工大学学报(自然科学版), 2017, (3): 389-396. doi: 10.14135/j.cnki.1006-3080.2017.03.015
YUE Yu-fei, LUO Jian-xu. An Application of Improved SOM Neural Network in Fault Diagnosis of Wastewater Treatment[J]. Journal of East China University of Science and Technology, 2017, (3): 389-396. doi: 10.14135/j.cnki.1006-3080.2017.03.015
Citation: YUE Yu-fei, LUO Jian-xu. An Application of Improved SOM Neural Network in Fault Diagnosis of Wastewater Treatment[J]. Journal of East China University of Science and Technology, 2017, (3): 389-396. doi: 10.14135/j.cnki.1006-3080.2017.03.015

一种改进的SOM神经网络在污水处理故障诊断中的应用

doi: 10.14135/j.cnki.1006-3080.2017.03.015
基金项目: 

国家自然科学基金(61304071)

详细信息
  • 中图分类号: TP183

An Application of Improved SOM Neural Network in Fault Diagnosis of Wastewater Treatment

  • 摘要: 自组织映射(SOM)神经网络初始权值的选取对神经网络的性能有重要的影响。采用改进的帝国竞争算法(ⅡCA)优化局部权重失真指数(LWDI)寻优SOM神经网络的初始权值;利用改进后的SOM神经网络(ⅡCA-SOM)对污水处理过程数据进行聚类和故障诊断。实验结果表明,与传统的SOM算法相比,ⅡCA-SOM算法取得了更好的聚类效果,且故障诊断的误诊率更低。

     

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出版历程
  • 收稿日期:  2016-10-12
  • 刊出日期:  2017-06-30

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