摘要
The enlarged production scale of underground non-ferrous metal mines is affected by many uncertain factors difficult to describe mathematically with any level of accuracy.The problem can be solved by a synthesis of artificial intelligence.Based on the analysis of the major factors affecting the scale of enlarged production,we first interpreted in detail the design principles and structure of the intelligent system.Secondly,we introduced an ANN subsystem.In order to ensure technological and scale efficien- cies of the training samples for ANN,we filtrated the samples with a DEA method.Finally,we trained the intelligent system,which was proved to be very efficient.
The enlarged production scale of underground non-ferrous metal mines is affected by many uncertain factors difficult to describe mathematically with any level of accuracy. The problem can be solved by a synthesis of artificial intelligence. Based on the analysis of the major factors affecting the scale of enlarged production, we first interpreted in detail the design principles and structure of the intelligent system. Secondly, we introduced an ANN subsystem. In order to ensure technological and scale efficiencies of the training samples for ANN, we filtrated the samples with a DEA method. Finally, we trained the intelligent system, which was proved to be very efficient.
基金
Project 50374005 supported by the National Natural Science Foundation of China
