期刊文献+

制冷系统仿真中定量参数的神经网络辨识 被引量:7

Artificial Neural Networks Identification of Quantitative Coefficients in Refrigeration System Simulation
下载PDF
导出
摘要 尝试用现代人工智能技术来改进现有的制冷系统仿真方法.首先,提炼出与制冷系统仿真结果的量化密切相关的定量参数,然后在已有的定性数值仿真模型的基础上,根据实验数据,采用人工神经网络(ANN)方法对仿真模型中的定量参数进行辨识,识别出最佳的定量参数.这不仅有利于提高仿真精度,改善计算稳定性,而且降低了对仿真软件用户的技术要求,有利于仿真技术的实用化.对房间空调器稳态特性仿真的初步结果表明该方法效果良好. The modern artificial intelligent (AI) technology was utilized in simulation of the refrigeration system.At first,the quantitative coefficients which are sensitive to the quantitative simulation results were persented.Then,based on the qualitative numerical simulation models,artificial neural network(ANN) was used to identify the quantitative coefficients in the simulation models accorrding to the experimental data.Finally,the simulation software with the identified quantitative coefficients was used to predict the behaviors of the actual refrigeration system.The new method can improve the simulation precision and stability of computation,and lower the professional demands of using the software.The recommended method is more promising than the current simulation methods in practical use.An example simulating the steady state performances of a room air conditioner was given which shows the satisfactory effects.
出处 《上海交通大学学报》 EI CAS CSCD 北大核心 1999年第8期939-941,共3页 Journal of Shanghai Jiaotong University
基金 国家教委回国留学人员基金 上海交通大学科技发展基金
关键词 制冷系统 定量参数 空调器 仿真 神经网络辨识 refrigeration system intelligent simulation identification artificial neural networks(ANN) quantitative coefficients
  • 相关文献

参考文献2

二级参考文献5

  • 1陈芝久.制冷系统热动力学初探[J].制冷学报,1987,(4).
  • 2Dhar M and Soedel W. Transient analysis of a vapor compression refrigeratlon system. XV I R Cong,Venice,Italy, 1979.
  • 3Chi J,Didion D. A simulation of the transient performance of a heat pump. Int J Refrig, 1982,5(3): 176-184.
  • 4Jiang Y,Li J S,Yang X D. Fault direction space method for on-Line fault detection ASHRAE Trans , 1995, 101 (2) : 219-228.
  • 5巫江虹,王世平,陈长青,吴业正.神经网络系统理论在换热器分配特性研究中的应用[J].制冷学报,1997,18(3):1-5. 被引量:2

共引文献37

同被引文献74

引证文献7

二级引证文献46

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部