State diagnosis of ship sewage treatment equipment based on RBF neural networks

CAO Xiao-li1; HU Wei-jun1; SUN Huai-yi2; YANG Guang-xiang1; JIANG Chao-yuan1

doi:10.13873/j.1000-97872010.10.026

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State diagnosis of ship sewage treatment equipment based on RBF neural networks

更新时间：2025-09-11

- State diagnosis of ship sewage treatment equipment based on RBF neural networks
- Vol. 29, Issue 10, Pages: 21-23+27(2010)
- 作者机构：
  
  1. 重庆工商大学计算机科学与信息工程学院
  2. 重庆工业自动化仪表研究所
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872010.10.026
  CLC： U664.92;TP183
- Published：2010
- 稿件说明：
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[1]曹晓莉,胡卫军,孙怀义,杨光祥,江朝元.基于RBF神经网络的船用污水处理装置状态诊断[J].传感器与微系统,2010,29(10):21-23+27.

CAO Xiao-li1, HU Wei-jun1, SUN Huai-yi2, et al. State diagnosis of ship sewage treatment equipment based on RBF neural networks[J]. 2010, 29(10): 21-23+27.
[1]曹晓莉,胡卫军,孙怀义,杨光祥,江朝元.基于RBF神经网络的船用污水处理装置状态诊断[J].传感器与微系统,2010,29(10):21-23+27. DOI： 10.13873/j.1000-97872010.10.026.

CAO Xiao-li1, HU Wei-jun1, SUN Huai-yi2, et al. State diagnosis of ship sewage treatment equipment based on RBF neural networks[J]. 2010, 29(10): 21-23+27. DOI： 10.13873/j.1000-97872010.10.026.

摘要

研究应用径向基函数（RBF）神经网络对船用污水处理装置进行状态诊断

以提高船用污水处理装置状态诊断正确率。在分析了RBF神经网络基本结构和原理的基础上

设计一种诊断船用污水处理装置状态的三层RBF神经网络。通过采用实际监测的数据实现RBF神经网络诊断处理

结果表明:RBF神经网络的分类诊断效果较佳

能够有效地对船用污水处理装置状态进行诊断。

Abstract

A state diagnosis method of ship sewage treatment equipment adopting radial-basis function（RBF）neural network is introduced to improve the state diagnosis correctness.Based on the analysis on the structure and fundamental principle of RBF neural network

a three-layer RBF neural network is designed to diagnose the state of ship sewage treatment equipment.RBF neural network diagnosis is realized utilizing the practical monitoring data from a ship.It is proved by diagnosis results that RBF neural network has good effect of classification which can be used to diagnose state of ship sewage treatment equipment effectively.

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references

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