摘要
离心式压缩机防喘振控制系统具有复杂非线性、高精度控制要求等特点,传统的控制方式难以实现理想的控制效果。针对这一问题,将模糊控制系统中方便的知识抽取表达和神经网络的自适应学习与并行计算等功能有机结合,得到了性能更加完善的模糊神经网络系统。仿真分析表明,采用模糊神经网络控制可显著提升离心式压缩机防喘振控制系统的工作性能,比模糊控制具有更小的超调量和振荡。
It has been generally recognized that traditional methods can hardly achieve satisfactory control effect in Centrifugal Compressor Antisurge Control System（CCACS）,for its specific characteristics of complex non-linearity and high precision control requirements.A Fuzzy Neural Network System（FNNS） with more perfect performance was obtained through combination of fuzzy controlling and neural network,which took each advantages of convenient knowledge abstracting expression and adaptive learning function,respectively.The simulation analysis shows that service behavior of CCACS can be significantly promoted with application of FNNS,coming up to a much smaller overshoot and oscillation compared to that of fuzzy controlling.
出处
《煤矿机械》
北大核心
2011年第10期81-83,共3页
Coal Mine Machinery
基金
校级科研项目(QY0616)
