关键词: 巷道变形预测, RBF神经网络, 贝叶斯阴阳和谐学习算法, 对角型广义RBF神经网络

Abstract: The structural parameters of the hidden layer node number and connection weights of the classical RBF neural network model are obtained by experiences,so,the performance of the classical RBF neural network model is depends on the subjectivity of experts,that is to say,there are certain blindness and randomness are existed,which lead to predict the roadway deformation with great difficult.In order to improve the performance of the classical RBF neural network model,the structural parameters of the hidden layer node number and connection weights are optimized by adopting the Bayesian ying-yang harmonly learning algorithm,a new roadway deformation prediction model based on improved BRF neural network is proposed,the improved neural network model can be names as diagonal type generalized RBF neural network model.The experiment is done based on the on-site long-term monitoring data of the fully-mechanized sublevel stoping roadways of Lu'an mining area and Yanzhou mining area to analyze the performance of the classical RBF neural network model and diagonal type generalized RBF neural network model,the results show that:①the prediction precise of roof and floor roadway deformation of the diagonal type generalized RBF neural network model is about 92.2%,while the prediction precise of the classical RBF neural network model is about 80.6%;②the prediction precise of coal side wall deformation of the diagonal type generalized RBF neural network model is about 90.2%,while the prediction precise of the classical RBF neural network model is about 78.6%.The above experimental results further show that the prediction of roadway deformation can be done with high precise by the diagonal type generalized RBF neural network model,which can provide some reference for the prediction of roadway deformation with high precise.

Key words: Prediction of roadway deformation, RBF neural network, Bayesian ying-yang harmonly learning algorithm, Diagonal type generalized RBF neural network