摘要
针对火电控制过程产生的数据连续性强、复杂度高,循环神经网络模型行为与实际控制过程难以建立语义关联,不能直观地进行模型的调试、优化和语义上的分析等问题,将可视分析技术引入面向系统辨识的循环神经网络建模过程中,提出可视分析系统iaRNN.首先,通过可视化隐藏单元激活值分布与覆盖范围设计模型评估组合视图,支持内外结合多方面评价模型性能;然后,从时序关系演变和敏感性分析等角度设计可视化视图,以支持探索模型对控制参数的响应行为;最后,基于序列符号化和聚类分析提出了一种用于探索强时序依赖的实值时间序列与隐藏单元关联模式的可视化方法.使用电厂真实数据进行案例分析,验证了iaRNN在辅助用户理解模型工作机理和诊断模型缺陷方面的有效性.
Due to the problems such as strong continuity and high complexity of the data generated by the thermal power control process,and the difficulty of establishing a semantic correlation between the model behavior of recurrent neural networks and the actual control process,the model debugging,optimization and semantic analysis cannot be carried out intuitively.Visual analytics in recurrent neural networks modeling for system identification is applied and a visual analysis system called iaRNN is proposed.Firstly,by visu-alizing the activation value distribution and coverage of hidden units,the combined view for model evalua-tion is designed to support the evaluation of model performance in many aspects including inside and out-side.Then,visual views are designed from the perspectives of temporal evolution,sensitivity analysis,etc,to support the exploration of model response behavior to thermal power control parameters.Finally,based on the symbolic representation of time sequences and clustering analysis,a method for exploring association patterns between strong time-dependent real-valued time series and hidden units is proposed.A case study using real power plant data is conducted to verify the effectiveness of iaRNN in assisting users to understand the working mechanism of the model and diagnose model defects.
作者
纪连恩
杨云
邱诗荣
王艺
田彬
Ji Lian’en;Yang Yun;Qiu Shirong;Wang Yi;Tian Bin(Beijing Key Laboratory of Petroleum Data Mining,China University of Petroleum,Beijing 102249;Department of Computer Science and Technology,China University of Petroleum,Beijing 102249;Beijing Engineering Research Center of Power Station Automation,Beijing 102211;China Energy Zhishen Control Technology Company Limited,Beijing 102211)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2021年第12期1876-1886,共11页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(60873093)
国家能源集团2030重大项目(GJNY2030XDXM-19-08.2).
关键词
可视分析
循环神经网络
系统辨识
火电控制
visual analysis
recurrent neural networks
system identification
thermal power control
