基于数据的改进回声状态网络在高炉煤气发生量预测中的应用

Improved Echo State Network Based on Data-driven and Its Application to Prediction of Blast Furnace Gas Output

以钢铁企业高炉煤气系统这一复杂生产过程为背景,针对高炉煤气发生量的预测问题,提出一种基于数据的网络模型预测方法.鉴于生产数据含噪高的特点,采用经验模态分解将历史数据分解为若干独立的固有模态函数,将小尺度函数经低通滤波器自适应去噪后,再对数据重构以建立预测模型.在建模过程中提出一种改进的回声状态网络,通过奇异值分解求取网络输出权值,克服了线性回归算法出现的病态问题,提高了模型的预测精度.现场实际数据预测结果表明所提出方法的有效性,为制定煤气管网平衡调度方案提供科学的决策支持.

Based on the complex process of blast furnace gas （BFG） system in steel industry, a network forecasting method based on data-driven is established in this paper for the prediction problem on BFG output. Since the practical data include a diversity of noises, an empirical mode decomposition approach is employed to decompose the time series signal into a group of independent intrinsic mode functions, and the formed small-scale intrinsic mode functions are denoised by low-pass filter with an adaptive threshold. Then, the re-constructed signals are used to build the forecasting model, in which an improved echo state network is proposed and the network output weights are obtained by singular value decomposition. Therefore, the ill-conditioned problem of previous linear regression is overcome and the forecasting precision is increased. The prediction results using practical production data show the validity of the proposed method and also provide the scientific decision support for the gas resources scheduling.