Rock Mass Deformation Long-Term Forecast via DRNN Neural Network Model

Abstract

The question of rock mass deformation Long-term forecast is researched base on DRNN. The construction of neural network is optimized via reconstructed chaotic phase space, and the all nodes of DRNN are interconnected, and the feedback between nodes and that of node itself is included, and mult-linkage branch is build between two nerves, and the linkage branch stands for the link weight and the time delay of regular step. So the current moment network output of node depends on not only current moment network iutput, but also the node output of Some moment before current, so the chaotic prediction sensibility to initial condition is reduced effectively. The calculating velocity and network stability is improved effectively. Examples show that results are reasonable and the long-term prediction is reasonable and feasible.