A brain-like multi-hierarchical modular neural network with applications to gas concentration forecasting

This paper presents a novel modular neural network called brain-like multi-hierarchical modular network (BMNN). Unlike most of the traditional modular neural network, the BMNN has a brain-like multi-hierarchical structure and uses a collaborative learning approach. In BMNN learning process, each input sample is learned by multiple sub-sub-modules in different sub-modules and the learning result of BMNN is the integration of the multiple sub-sub-modules learning results, which helps to improve the BMNN´s learning accuracy and generalization ability. The learning algorithm of the sub-sub-modules is an algebraic method which greatly improves the BMNN´s learning speed. Applied BMNN to mine gas concentration forecasting based on the practical production data, the forecasting results compared with BP neural network and RBF neural network, the experiment results show the validity of the proposed forecasting method and can provide the scientific decision for the safety in coal mine production.