A Constructive Neural Network Learning Method Based on Quotient Space and Its Application in Coal Mine Gas Prediction

摘要

This paper uses constructive neural network learning approach to predict gas concentrations, under the framework of quotient space granular computing model. Using quotient space granular computing theory, the problem can be macro-level analysis - examining different particle size between the quotient space conversion, movement, interdependent relations, and the original features of the database information to build grain size, using a variety of granularity, from different levels of analysis of complex gas data makes the learning characteristics of the sample is more obvious, in order to better meet the requirements of machine learning. Constructive neural network learning method achieves the data mining of different particle size structure the quotient space from the micro. At last, the method is applied to predict gas concentration, and the satisfying results are achieved. It is expected that Constructive Neural Network Learning Method will have wide applications.