Directing at the non-linear dynamic characteristics of water inrush from coal seam floor and by the analysis of the shortages of current forecast methods for water inrush from coal seam floor, a new forecast method wa...Directing at the non-linear dynamic characteristics of water inrush from coal seam floor and by the analysis of the shortages of current forecast methods for water inrush from coal seam floor, a new forecast method was raised based on wavelet neural network （WNN） that was a model combining wavelet function with artificial neural network. Firstly basic principle of WNN was described, then a forecast model for water inrush from coal seam floor based on WNN was established and analyzed, finally an example of forecasting the quantity of water inrush from coal floor was illustrated to verify the feasibility and superiority of this method. Conclusions show that the forecast result based on WNN is more precise and that using WNN model to forecast the quantity of water inrush from coal seam floor is feasible and practical.展开更多
In this paper,an application of a nonlinear predictive controller based on a self recurrent wavelet network (SRWN) model for a direct internal reforming solid oxide fuel cell (DIR-SOFC) is presented. As operating temp...In this paper,an application of a nonlinear predictive controller based on a self recurrent wavelet network (SRWN) model for a direct internal reforming solid oxide fuel cell (DIR-SOFC) is presented. As operating temperature and fuel utilization are two important parameters,the SOFC is identified using an SRWN with inlet fuel flow rate,inlet air flow rate and current as inputs,and temperature and fuel utilization as outputs. To improve the operating performance of the DIR-SOFC and guarantee proper operating conditions,the nonlinear predictive control is implemented using the off-line trained and on-line modified SRWN model,to manipulate the inlet flow rates to keep the temperature and the fuel utilization at desired levels. Simulation results show satisfactory predictive accuracy of the SRWN model,and demonstrate the excellence of the SRWN-based predictive controller for the DIR-SOFC.展开更多
文摘Directing at the non-linear dynamic characteristics of water inrush from coal seam floor and by the analysis of the shortages of current forecast methods for water inrush from coal seam floor, a new forecast method was raised based on wavelet neural network （WNN） that was a model combining wavelet function with artificial neural network. Firstly basic principle of WNN was described, then a forecast model for water inrush from coal seam floor based on WNN was established and analyzed, finally an example of forecasting the quantity of water inrush from coal floor was illustrated to verify the feasibility and superiority of this method. Conclusions show that the forecast result based on WNN is more precise and that using WNN model to forecast the quantity of water inrush from coal seam floor is feasible and practical.
基金supported by the National High-Tech Research and Devel-opment Program (863) of China (No. 2006AA05Z148)the Shanghai Municipal Natural Science Foundation, China (No. 08ZR1409800)
文摘In this paper,an application of a nonlinear predictive controller based on a self recurrent wavelet network (SRWN) model for a direct internal reforming solid oxide fuel cell (DIR-SOFC) is presented. As operating temperature and fuel utilization are two important parameters,the SOFC is identified using an SRWN with inlet fuel flow rate,inlet air flow rate and current as inputs,and temperature and fuel utilization as outputs. To improve the operating performance of the DIR-SOFC and guarantee proper operating conditions,the nonlinear predictive control is implemented using the off-line trained and on-line modified SRWN model,to manipulate the inlet flow rates to keep the temperature and the fuel utilization at desired levels. Simulation results show satisfactory predictive accuracy of the SRWN model,and demonstrate the excellence of the SRWN-based predictive controller for the DIR-SOFC.
