构造性神经网络在煤矿瓦斯预测中的应用

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

在商空间粒度计算理论框架下,用构造性神经网络学习方法进行瓦斯浓度预测。采用商空间粒度计算理论,可以通过对问题进行宏观分析——研究不同粒度商空间之间的转换、运动、依存的关系,并对数据库中的原始特征信息进行粒度构建,采用多种粒度,从不同的层次分析复杂的瓦斯数据信息使得学习样本的特征更加明显,以更好地满足机器学习的要求。构造性神经网络学习方法则可以从微观上对具有不同粒度结构的商空间进行数据挖掘。最后将该方法应用于瓦斯浓度预测,取得了较好的结果.这表明了基于商空间的构造性神经网络学习方法的可行性和应用前景。

Under the framework of quotient space granular computing model,using constructive neural network learning approach to predict gas concentrations. 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 can be different from the micro-size structures on the quotient space data mining. 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.