Notice of RetractionEvaluation on the Stability of Rock Mass Deformation Based on Chaotic Muation -RPNN Model
To analysis the stability of rock mass long-term developing deformation,the chaotic muation -RPNN model is built. Combining the chaos theory,the muation theory is introduced, and the stability of rock mass deformation is evaluated by chaotic muation -RPNN model. The lost measured displacement data are interpolated by using the higher order polynomial methos, and the long-term displacement is forecasted,and the stability of rock mass long-term developing deformation is analyzed. Research shows that the strend of predicted displacement is approximately the same as that of measuring displacement,and there is no suddenly dropped or sudden increased phenomena,and the displacement increasing strend of some measuring point remains stable with Lower growth trend, and the deformation of rock mass is strending to be stable.
Long-Term Prediction Model of Rock Mass Displacement Based on Chaotic-Dynamic Time Delay BP Neutral Network
The chaos-dynamic time delay BP neural network model is built to realize long-term prediction of rock mass displacement of large underground grave,and fast analyze long-time stability of surrounding rock through optimized structure of BP neural network coupling with chaotic-dynamic parameters of displacement. Embedding dimension is set as the number of input layer, and predicting feedback mode of neural network is added, and prediction training samples are generated dynamically, so long-term forecasting are enabled. The relative bigger delay time is selected, then time delay between adjacent phase points is when forecasting step is, so the long-time prediction is realized by limited number of forecasting steps. The instances show that computational stability of built prediction model is preferable with faster calculating speed, and prediction precision is all within 10% when predicting step is not more than 5, and the number of forecasting displacements is not more than 10. Terefore the forecasting results are real time and effective, the long-term prediction of rock mass displacement is realized.
Chaotic time series forecasting based on dynamic recurrent neural networks model
A Dynamic Recurrent Neural Networks(DRNN) model was presented in this paper to forecast chaotic time series.The input dimension of DRNN was decided by minimal embedding dimension.The training samples were generated by means of the stepping recursive phase points.It can improve precision and stability of prediction to use chaotic characteristic to deal with samples and mapping nonlinear function by DRNN.The DRNN model was applied to simulation of Lorenz system and shot-term forecasting of Shanghai stock index.Compared with the traditional standard BP neural network,this proposed model shows higher precision.Therefore,this research proves the effectiveness of the proposed model in the practical prediction of time series.