Molding and simulation of time series prediction based on dynamic neural network(NN) are studied. Prediction model for non-linear and time-varying system is proposed based on dynamic Jordan NN. Aiming at the intrinsic...Molding and simulation of time series prediction based on dynamic neural network(NN) are studied. Prediction model for non-linear and time-varying system is proposed based on dynamic Jordan NN. Aiming at the intrinsic defects of back-propagation (BP) algorithm that cannot update network weights incrementally, a hybrid algorithm combining the temporal difference (TD) method with BP algorithm to train Jordan NN is put forward. The proposed method is applied to predict the ash content of clean coal in jigging production real-time and multi-step. A practical example is also given and its application results indicate that the method has better performance than others and also offers a beneficial reference to the prediction of nonlinear time series.展开更多
This study aims to predict the migration time of toxic fumes induced by excavation blasting in underground mines.To reduce numerical simulation time and optimize ventilation design,several back propagation neural netw...This study aims to predict the migration time of toxic fumes induced by excavation blasting in underground mines.To reduce numerical simulation time and optimize ventilation design,several back propagation neural network(BPNN)models optimized by honey badger algorithm(HBA)with four chaos mapping(CM)functions(i.e.,Chebyshev(Che)map,Circle(Cir)map,Logistic(Log)map,and Piecewise(Pie)map)are developed to predict the migration time.125 simulations by the computational fluid dynamics(CFD)method are used to train and test the developed models.The determination coefficient(R2),the variance accounted for(VAF),the Willmott’s index(WI),the root mean square error(RMSE),the mean absolute percentage error(MAPE),and the sum of squares error(SSE)are utilized to evaluate the model performance.The evaluation results indicate that the CirHBA-BPNN model has achieved the most satisfactory performance by reaching the highest values of R2(0.9945),WI(0.9986),VAF(99.4811%),and the lowest values of RMSE(15.7600),MAPE(0.0343)and SSE(6209.4),respectively.The wind velocity in roadway(Wv)is the most important feature for predicting the migration time of toxic fumes.Furthermore,the intrinsic response characteristic of the optimal model is implemented to enhance the model interpretability and provide reference for the relationship between features and migration time of toxic fumes in ventilation design.展开更多
文摘Molding and simulation of time series prediction based on dynamic neural network(NN) are studied. Prediction model for non-linear and time-varying system is proposed based on dynamic Jordan NN. Aiming at the intrinsic defects of back-propagation (BP) algorithm that cannot update network weights incrementally, a hybrid algorithm combining the temporal difference (TD) method with BP algorithm to train Jordan NN is put forward. The proposed method is applied to predict the ash content of clean coal in jigging production real-time and multi-step. A practical example is also given and its application results indicate that the method has better performance than others and also offers a beneficial reference to the prediction of nonlinear time series.
基金The authors were funded by China Scholarship Council(Grant Nos.202106370038,and 201906690049)National Key Research and Development Program of China(Grant No.2021YFC3001300).
文摘This study aims to predict the migration time of toxic fumes induced by excavation blasting in underground mines.To reduce numerical simulation time and optimize ventilation design,several back propagation neural network(BPNN)models optimized by honey badger algorithm(HBA)with four chaos mapping(CM)functions(i.e.,Chebyshev(Che)map,Circle(Cir)map,Logistic(Log)map,and Piecewise(Pie)map)are developed to predict the migration time.125 simulations by the computational fluid dynamics(CFD)method are used to train and test the developed models.The determination coefficient(R2),the variance accounted for(VAF),the Willmott’s index(WI),the root mean square error(RMSE),the mean absolute percentage error(MAPE),and the sum of squares error(SSE)are utilized to evaluate the model performance.The evaluation results indicate that the CirHBA-BPNN model has achieved the most satisfactory performance by reaching the highest values of R2(0.9945),WI(0.9986),VAF(99.4811%),and the lowest values of RMSE(15.7600),MAPE(0.0343)and SSE(6209.4),respectively.The wind velocity in roadway(Wv)is the most important feature for predicting the migration time of toxic fumes.Furthermore,the intrinsic response characteristic of the optimal model is implemented to enhance the model interpretability and provide reference for the relationship between features and migration time of toxic fumes in ventilation design.
