In order to predict the danger of coal and gas outburst in mine coal layer correctly, on the basis of the VLBP and LMBP algorithm in Matlab neural network toolbox, one kind of modified BP neural network was put forth ...In order to predict the danger of coal and gas outburst in mine coal layer correctly, on the basis of the VLBP and LMBP algorithm in Matlab neural network toolbox, one kind of modified BP neural network was put forth to speed up the network convergence speed in this paper. Firstly, according to the characteristics of coal and gas outburst, five key influencing factors such as excavation depth, pressure of gas, and geologic destroy degree were selected as the judging indexes of coal and gas outburst. Secondly, the prediction model for coal and gas outburst was built. Finally, it was verified by practical examples. Practical application demonstrates that, on the one hand, the modified BP prediction model based on the Matlab neural network toolbox can overcome the disadvantages of constringency and, on the other hand, it has fast convergence speed and good prediction accuracy. The analysis and computing results show that the computing speed by LMBP algorithm is faster than by VLBP algorithm but needs more memory. And the resuits show that the prediction results are identical with actual results and this model is a very efficient prediction method for mine coal and gas outburst, and has an important practical meaning for the mine production safety. So we conclude that it can be used to predict coal and gas outburst precisely in actual engineering.展开更多
An artificial neural network (ANN) short term forecasting model of consumption per hour was built based on seasonality,trend and randomness of a city period of time water consumption series.Different hidden layer no...An artificial neural network (ANN) short term forecasting model of consumption per hour was built based on seasonality,trend and randomness of a city period of time water consumption series.Different hidden layer nodes,same inputs and forecasting data were selected to train and forecast and then the relative errors were compared so as to confirm the NN structure.A model was set up and used to forecast concretely by Matlab.It is tested by examples and compared with the result of time series trigonometric function analytical method.The result indicates that the prediction errors of NN are small and the velocity of forecasting is fast.It can completely meet the actual needs of the control and run of the water supply system.展开更多
This paper describes a new, highly modular, simulation tool named ＂PVLab＂ and developed by the GREMAN laboratory. It is designed to assist the designer in the sizing ofPV （photovoltaic） installations. The programm...This paper describes a new, highly modular, simulation tool named ＂PVLab＂ and developed by the GREMAN laboratory. It is designed to assist the designer in the sizing ofPV （photovoltaic） installations. The programming structure and physical models implemented within this tool are described, and several case studies are proposed to highlight its relevance. The predicted yearly electrical energy production of grid-connected PV plants is discussed. In particular, the predicted performance of such plants is compared with that given by the PVsyst software. PVLab has a high level of flexibility, allowing its physical models and databases （e.g., meteorological data） to be modified according to the user＇s needs. This is made possible through the use of expertise applied to all of the computing steps, and to the MATLAB development environment. The user＇s ability to control the source code itself will allow much greater progress to be made in the field of renewable energy applications than with PVsyst, which is currently the commercial reference.展开更多
This paper proposes a numerical method for the study of ventilation efficiency in buildings. The developed model is validated with the experimental results of Nielsen who tested the isothermal flow in a scaled model o...This paper proposes a numerical method for the study of ventilation efficiency in buildings. The developed model is validated with the experimental results of Nielsen who tested the isothermal flow in a scaled model of a ventilated room. A zonal method is used to predict airflow patterns in the same ventilated room. The different equations governing the flow in the room were coded in Matlab for different operating conditions, different zonal configurations of the room and different number of cells （control volumes）. The efficiency of the ventilation was determined by calculating the number of ACH （air changes per hour） for each cell. The present results show the importance of the inlet air flow rate, the space resolution and the jet inlet dimensions on the determination of air quality.展开更多
Six main influencing factors： slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness, were selected by Grey correlation theory and Grey relational analysis procedure programmed by th...Six main influencing factors： slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness, were selected by Grey correlation theory and Grey relational analysis procedure programmed by the MATLAB software package to select the surface movement and deformation parameters. On this basis, the paper built a BP neural network model that takes the six main influencing factors as input data and corresponding value of ground subsidence as output data. Ground subsidence of the 3406 mining face in Haoyu Coal was predicted by the trained BP neural network. By comparing the prediction and the practices, the research shows that it is feasible to use the 13P neural network to predict mountain mining subsidence.展开更多
基金Supported by the National Natural Science Foundation Project（50604008） and Scientific Research Fund of Hunan Provincial Education Department（06B029）, China Postdoctoral Science Foundation Project（2005038559）
文摘In order to predict the danger of coal and gas outburst in mine coal layer correctly, on the basis of the VLBP and LMBP algorithm in Matlab neural network toolbox, one kind of modified BP neural network was put forth to speed up the network convergence speed in this paper. Firstly, according to the characteristics of coal and gas outburst, five key influencing factors such as excavation depth, pressure of gas, and geologic destroy degree were selected as the judging indexes of coal and gas outburst. Secondly, the prediction model for coal and gas outburst was built. Finally, it was verified by practical examples. Practical application demonstrates that, on the one hand, the modified BP prediction model based on the Matlab neural network toolbox can overcome the disadvantages of constringency and, on the other hand, it has fast convergence speed and good prediction accuracy. The analysis and computing results show that the computing speed by LMBP algorithm is faster than by VLBP algorithm but needs more memory. And the resuits show that the prediction results are identical with actual results and this model is a very efficient prediction method for mine coal and gas outburst, and has an important practical meaning for the mine production safety. So we conclude that it can be used to predict coal and gas outburst precisely in actual engineering.
基金Supported by Foundation for University Key Teacher by Ministryof Education.
文摘An artificial neural network (ANN) short term forecasting model of consumption per hour was built based on seasonality,trend and randomness of a city period of time water consumption series.Different hidden layer nodes,same inputs and forecasting data were selected to train and forecast and then the relative errors were compared so as to confirm the NN structure.A model was set up and used to forecast concretely by Matlab.It is tested by examples and compared with the result of time series trigonometric function analytical method.The result indicates that the prediction errors of NN are small and the velocity of forecasting is fast.It can completely meet the actual needs of the control and run of the water supply system.
文摘This paper describes a new, highly modular, simulation tool named ＂PVLab＂ and developed by the GREMAN laboratory. It is designed to assist the designer in the sizing ofPV （photovoltaic） installations. The programming structure and physical models implemented within this tool are described, and several case studies are proposed to highlight its relevance. The predicted yearly electrical energy production of grid-connected PV plants is discussed. In particular, the predicted performance of such plants is compared with that given by the PVsyst software. PVLab has a high level of flexibility, allowing its physical models and databases （e.g., meteorological data） to be modified according to the user＇s needs. This is made possible through the use of expertise applied to all of the computing steps, and to the MATLAB development environment. The user＇s ability to control the source code itself will allow much greater progress to be made in the field of renewable energy applications than with PVsyst, which is currently the commercial reference.
文摘This paper proposes a numerical method for the study of ventilation efficiency in buildings. The developed model is validated with the experimental results of Nielsen who tested the isothermal flow in a scaled model of a ventilated room. A zonal method is used to predict airflow patterns in the same ventilated room. The different equations governing the flow in the room were coded in Matlab for different operating conditions, different zonal configurations of the room and different number of cells （control volumes）. The efficiency of the ventilation was determined by calculating the number of ACH （air changes per hour） for each cell. The present results show the importance of the inlet air flow rate, the space resolution and the jet inlet dimensions on the determination of air quality.
文摘Six main influencing factors： slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness, were selected by Grey correlation theory and Grey relational analysis procedure programmed by the MATLAB software package to select the surface movement and deformation parameters. On this basis, the paper built a BP neural network model that takes the six main influencing factors as input data and corresponding value of ground subsidence as output data. Ground subsidence of the 3406 mining face in Haoyu Coal was predicted by the trained BP neural network. By comparing the prediction and the practices, the research shows that it is feasible to use the 13P neural network to predict mountain mining subsidence.
