A Novel Echo State Network and Its Application in Temperature Prediction of Exhaust Gas From Hot Blast Stove
Ying Yang, Zhao Xin, Xiaozhi Liu
Abstract
Hot blast stove (HBS) provides hot air for the blast furnace and temperature prediction for its exhaust gas is of vital importance to control the process. In this article, a novel deep memory echo state network (DMESN) is proposed for temperature prediction. First, data preprocessing is performed, including outlier rejecting, missing data handling and lag time calculating, so that better dynamic characteristics of data set can be obtained. Then, in order to improve the prediction accuracy, an improved hidden layer structure is proposed, which consists of two parts: echo state formation and hidden state formation. Echo state formation is used in network starting phase. Aiming at the problem that information cannot be effectively retained, three gates are designed in the cell to obtain a stable echo state which will be used to initialize the hidden state. In this way, the hidden state can operate continuously and stably. Finally, comparing experiments were carried out to demonstrate the efficiency of the proposed method. The simulation results show that DMESN achieves excellent performance in terms of accuracy, stability, and learning speed.