The Research Of The Recurrent Radial Basis Function Neural Network In Heat State Prediction Of Blast Furnace

With the rapid development of micro-electronics, computer, communication, Internet, message, automation technologies since the middle period of 20th century. The third tidal wave centered on message technology has come, which results in the best flourishing period of industry economic era, and opens the door of knowledge economic era. Human society has entered into the information era with the development and popularization of computer technology. The capacity of utilizing information to manufacture and collect data has improved greatly. Data mining technology has been the direction of prediction and establishing model.The Silicon content in the molten iron is regarded as a major prediction object based on manufacturing data of blast furnace. We must take the time relationship into account because there is a relationship between the input and output data sets(Temporal Data), which RRBF is used to establish the heat state prediction model in the paper. The self-connection neural unit has been added to RBF NN which made RBF NN had the capacity of memorizing the past-time data . The on-line learning method is also adopted in this paper. By predicting the content of silicon in molten iron the heat state of blast furnace is gained, which provide the fine condition to improve quality of steel and stabilize manufacturing craftwork.The ideas, features and operation of artifical neural network are introduced in detail. from the biology point and mathematics reasoning aspect, then it analyzes the architecture and training algorithm of RBF and RRBF. Finally, through simulation using Matlab, the dissertation proves that the RRBF network is better than traditional RBF neural network model in prediction precision and speed of training. Thus it can be seen that RRBF has obvious superiority in analyzing non- linear time-series data.