The hot compression tests of 7Mo super austenitic stainless(SASS)were conducted to obtain flow curves at the temperature of 1000-1200℃and strain rate of 0.001 s^(-1)to 1 s^(-1).To predict the non-linear hot deformati...The hot compression tests of 7Mo super austenitic stainless(SASS)were conducted to obtain flow curves at the temperature of 1000-1200℃and strain rate of 0.001 s^(-1)to 1 s^(-1).To predict the non-linear hot deformation behaviors of the steel,back propagation-artificial neural network(BP-ANN)with 16×8×8 hidden layer neurons was proposed.The predictability of the ANN model is evaluated according to the distribution of mean absolute error(MAE)and relative error.The relative error of 85%data for the BP-ANN model is among±5%while only 42.5%data predicted by the Arrhenius constitutive equation is in this range.Especially,at high strain rate and low temperature,the MAE of the ANN model is 2.49%,which has decreases for 18.78%,compared with conventional Arrhenius constitutive equation.展开更多
A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The prin...A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The principle of RPE learning algorithm is to adjust weights along the direction of Gauss-Newton. Meanwhile, it is unnecessary to calculate the second local derivative and the inverse matrixes, whose unbiasedness is proved. With application to the extremely short time prediction of large ship pitch, satisfactory results are obtained. Prediction effect of this algorithm is compared with that of auto-regression and periodical diagram method, and comparison results show that the proposed algorithm is feasible.展开更多
In order to forecast the strength of filling material exactly, the main factors affecting the strength of filling material are analyzed. The model of predicting the strength of filling material was established by appl...In order to forecast the strength of filling material exactly, the main factors affecting the strength of filling material are analyzed. The model of predicting the strength of filling material was established by applying the theory of artificial neural net- works. Based on cases related to our test data of filling material, the predicted results of the model and measured values are com- pared and analyzed. The results show that the model is feasible and scientifically justified to predict the strength of filling material, which provides a new method for forecasting the strength of filling material for paste filling in coal mines.展开更多
Excessive ground vibrations, due to blasting, can cause severe damages to the nearby area. Hence, the blast-induced ground vibration prediction is an essential tool for both evaluating and controlling the adverse cons...Excessive ground vibrations, due to blasting, can cause severe damages to the nearby area. Hence, the blast-induced ground vibration prediction is an essential tool for both evaluating and controlling the adverse consequences of blasting. Since there are several effective variables on ground vibrations that have highly nonlinear interactions, no comprehensive model of the blast-induced vibrations are available. In this study, the genetic expression programming technique was employed for prediction of the frequency of the adjacent ground vibrations. Nine input variables were used for prediction of the vibration frequencies at different distances from the blasting face. A high coefficient of determination with low mean absolute percentage error(MAPE) was achieved that demonstrated the suitability of the algorithm in this case. The proposed model outperformed an artificial neural network model that was proposed by other authors for the same dataset.展开更多
Based on the neural network technique, this paper proposes a BP neural network model which integrates geological factors which affect top coal caving in a comprehensive index. The index of top coal caving may be used ...Based on the neural network technique, this paper proposes a BP neural network model which integrates geological factors which affect top coal caving in a comprehensive index. The index of top coal caving may be used to forecast the mining cost of working faces, which shows the model’s potential prospect of applications.展开更多
Non-liner wave equation inversion,wavelet analysis and artificial neural networks were used to obtain stratum parameters and the distribution of thin coal seams.The lithology of the water-bearing/resisting layer in th...Non-liner wave equation inversion,wavelet analysis and artificial neural networks were used to obtain stratum parameters and the distribution of thin coal seams.The lithology of the water-bearing/resisting layer in the Quaternary system was also predicted.The implementation process included calculating the well log parameters,stratum contrasting the seismic data and the well logs,and extracting,studying and predicting seismic attributes.Seismic inversion parameters,including the layer velocity and wave impedance,were calculated and effectively used for prediction and analysis.Prior knowledge and seismic interpretation were used to remedy a dearth of seismic data during the inversion procedure.This enhanced the stability of the inversion method.Non-linear seismic inversion and artificial neural networks were used to interpret coal seismic lithology and to study the water-bearing/resisting layer in the Quaternary system.Interpretation of the 1～2 m thin coal seams,and also of the water-bearing/resisting layer in the Quaternary system,is provided.The upper mining limit can be lifted from 60 m to 45 m.The predictions show that this method can provide reliable data useful for thin coal seam exploitation and for lifting the upper mining limit,which is one of the principles of green mining.展开更多
文摘The hot compression tests of 7Mo super austenitic stainless(SASS)were conducted to obtain flow curves at the temperature of 1000-1200℃and strain rate of 0.001 s^(-1)to 1 s^(-1).To predict the non-linear hot deformation behaviors of the steel,back propagation-artificial neural network(BP-ANN)with 16×8×8 hidden layer neurons was proposed.The predictability of the ANN model is evaluated according to the distribution of mean absolute error(MAE)and relative error.The relative error of 85%data for the BP-ANN model is among±5%while only 42.5%data predicted by the Arrhenius constitutive equation is in this range.Especially,at high strain rate and low temperature,the MAE of the ANN model is 2.49%,which has decreases for 18.78%,compared with conventional Arrhenius constitutive equation.
文摘A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The principle of RPE learning algorithm is to adjust weights along the direction of Gauss-Newton. Meanwhile, it is unnecessary to calculate the second local derivative and the inverse matrixes, whose unbiasedness is proved. With application to the extremely short time prediction of large ship pitch, satisfactory results are obtained. Prediction effect of this algorithm is compared with that of auto-regression and periodical diagram method, and comparison results show that the proposed algorithm is feasible.
基金supported by the National Natural Science Foundation of China (No. 50490270, 50774077, 50574089, 50490273)the New Century Excellent Personnel Training Program of the Ministry of Education of China (No. NCET-06-0475)+1 种基金the Special Funds of Universities outstanding doctoral dissertation (No. 200760) the Basic Research Program of China (No. 2006CB202204-3)
文摘In order to forecast the strength of filling material exactly, the main factors affecting the strength of filling material are analyzed. The model of predicting the strength of filling material was established by applying the theory of artificial neural net- works. Based on cases related to our test data of filling material, the predicted results of the model and measured values are com- pared and analyzed. The results show that the model is feasible and scientifically justified to predict the strength of filling material, which provides a new method for forecasting the strength of filling material for paste filling in coal mines.
文摘Excessive ground vibrations, due to blasting, can cause severe damages to the nearby area. Hence, the blast-induced ground vibration prediction is an essential tool for both evaluating and controlling the adverse consequences of blasting. Since there are several effective variables on ground vibrations that have highly nonlinear interactions, no comprehensive model of the blast-induced vibrations are available. In this study, the genetic expression programming technique was employed for prediction of the frequency of the adjacent ground vibrations. Nine input variables were used for prediction of the vibration frequencies at different distances from the blasting face. A high coefficient of determination with low mean absolute percentage error(MAPE) was achieved that demonstrated the suitability of the algorithm in this case. The proposed model outperformed an artificial neural network model that was proposed by other authors for the same dataset.
文摘Based on the neural network technique, this paper proposes a BP neural network model which integrates geological factors which affect top coal caving in a comprehensive index. The index of top coal caving may be used to forecast the mining cost of working faces, which shows the model’s potential prospect of applications.
基金Projects 40574057 and 40874054 supported by the National Natural Science Foundation of ChinaProjects 2007CB209400 by the National Basic Research Program of ChinaFoundation of China University of Mining and Technology (OF4471)
文摘Non-liner wave equation inversion,wavelet analysis and artificial neural networks were used to obtain stratum parameters and the distribution of thin coal seams.The lithology of the water-bearing/resisting layer in the Quaternary system was also predicted.The implementation process included calculating the well log parameters,stratum contrasting the seismic data and the well logs,and extracting,studying and predicting seismic attributes.Seismic inversion parameters,including the layer velocity and wave impedance,were calculated and effectively used for prediction and analysis.Prior knowledge and seismic interpretation were used to remedy a dearth of seismic data during the inversion procedure.This enhanced the stability of the inversion method.Non-linear seismic inversion and artificial neural networks were used to interpret coal seismic lithology and to study the water-bearing/resisting layer in the Quaternary system.Interpretation of the 1～2 m thin coal seams,and also of the water-bearing/resisting layer in the Quaternary system,is provided.The upper mining limit can be lifted from 60 m to 45 m.The predictions show that this method can provide reliable data useful for thin coal seam exploitation and for lifting the upper mining limit,which is one of the principles of green mining.
