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中国工业与应用数学学会会刊
主管：中华人民共和国教育部
主办：西安交通大学
ISSN 1005-3085  CN 61-1269/O1
工程数学学报

工程数学学报 ›› 2015, Vol. 32 ›› Issue (6): 791-800.doi: 10.3969/j.issn.1005-3085.2015.06.001

• •    下一篇

基于人工免疫网络的汽轮发电机组运行状态评估与预测

董晓妮1,2,   温广瑞1,   张小栋1   

  1. 1- 西安交通大学机械工程学院，西安 710049
    2- 西安思源学院商学院，西安 710038
  • 收稿日期:2015-06-16 接受日期:2015-10-15 出版日期:2015-12-15 发布日期:2016-02-15
  • 基金资助:
    科技支疆项目(201491124).

Application of Artificial Immune Network in Turbo-generator Set Condition Assessment and Forecasting

DONG Xiao-ni1,2,   WEN Guang-rui1,   ZHANG Xiao-dong1   

  1. 1- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049
    2- School of Business, Xi'an Siyuan University, Xi'an 710038
  • Received:2015-06-16 Accepted:2015-10-15 Online:2015-12-15 Published:2016-02-15
  • Supported by:
    The Science and Technology Support Project in Xinjiang (201491124).

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摘要： 在分析、比较针对机械运行状态各种预测模型及方法的基础上，本文提出了一种基于人工免疫网络的预测模型，通过免疫网络调节与免疫规划，对神经网络系统进行设计与学习，得出人工免疫网络，建立了基于人工免疫网络的中长期预测模型．通过某汽轮发电机组状态中长期预测的应用，结果表明，该方法与传统的BP神经网络和径向基网络(RBF)模型预测方法相比，具有较强的自适应能力且预测效果好，可实现对机械运行状态的预测预报，为预知维修奠定技术基础．

关键词: 人工免疫网络, 系统状态预测, 免疫网络调节, 免疫规划, 神经网络

Abstract:

By analyzing and comparing the common models and methods of state forecasting, a novel neural network technique, artificial immune network (AIN) in state forecasting of dyna-mical system is proposed to deal with the prediction problem. This paper is mainly focused on the AIN immune adjustment and immune planning, the network system structure designing, and the final model building. In order to examine the feasibility of AIN in state forecasting, the practical vibration data measured from some turbo-generator set are used to validate the performance of the AIN model by comparing it with a traditional BP neural network and RBF network model. The experiment results show that the proposed AIN model outperforms the BP neural network and RBF neural network based on the criteria of normalized mean square error, and it can capture the system dynamic behavior quickly, and track system responses accurately.

Key words: artificial immune metwork, system state forecasting, immune network regulation, immune programming, neural network

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