Instability identification on large scale underground mined-out area in the metal mine based on the improved FRBFNN

Abstract

To identify the instability on large scale underground mined-out area in the metal mine effectively, the parameters of radial basis function were determined through clustering method and the improved fuzzy radial basis function neural network (FRBFNN) model of instability identification model about large scale underground mined-out area in the metal mine was built. The improved FRBFNN model was trained and tested. The results show that the improved FRBFNN model has high training accuracy and generalization ability. Parameters such as pillar area ratio, filling level and the value of rock quality designation have strong influence on instability of large scale underground mined-out area. Correctness of analysis about the improved FRBFNN model was proved by the practical application results about instability discrimination of surrounding rock in large-scale underground mined-out area of a metal mine in south China.