Application Of RBF Neural Network In Atuomatic Control System Of Fully Mechanized Sublevel Caving Working Face

With the development of technology, automation of fully mechanized sublevel caving working face now becomes a main trend of modern mining. Automation of fully mechanized sublevel caving working face could not only increase the mine's output, but also lighten workload and enhances work efficiency. According to a typical fully mechanized sublevel caving working face, this paper analyses the function of all equipments of this working face as a whole and researches the automatic control system of equipments of this working face with application of neural network theory. And this paper consist of the following several parts. 1.Introduction of neural network control theory; 2.Compare the fuction between RBF network and BP network; 3.Analyze and research the harmonious operation of equipments of fully mechanized sublevel caving working face, and divide the system into several parts based on the equipments' different function; 4. Study the automatic system of fully mechanized sublevel caving working face applying neural network control theory. By means of MALAB programming language, the automatic control of each equipment's work of working face was been simulinked. Research of automation system of fully mechanized sublevel caving working face is a very hard and complicated work including many fields and needing a lot of work. This paper just makes some elementary study that only does a small part comparing with the practice of automation system, and there is still plenty of work needed to do.