Objective To investigate the prediction effect of neural networks for seismic response of structure under the Levenberg Marquardt(LM) algorithm. Results Based on identification and prediction ability of neural netw...Objective To investigate the prediction effect of neural networks for seismic response of structure under the Levenberg Marquardt(LM) algorithm. Results Based on identification and prediction ability of neural networks for nonlinear systems, and combined with LM algorithm, a multi layer forward networks is adopted to predict the seismic responses of structure. The networks is trained in batch by the shaking table test data of three floor reinforced concrete structure firstly, then the seismic responses of structure are predicted under the unused excitation data, and the predict responses are compared with the experiment responses. The error curves between the prediction and the experimental results show the efficiency of the method. Conclusion LM algorithm has very good convergence rate, and the neural networks can predict the seismic response of the structure well.展开更多
Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic mo...Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters.Most machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box models.In addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source.These make using the ML models on new datasets difficult.This study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model.The data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for testing.Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best performance.The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases.Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP.Furthermore,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box model.Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.展开更多
Considering recent developments in the energy sector,further reduction of electricity cost and flattening of the electric power demand curve are needed.We have focused on an autonomous electric heater control system t...Considering recent developments in the energy sector,further reduction of electricity cost and flattening of the electric power demand curve are needed.We have focused on an autonomous electric heater control system that can easily be implemented in existing buildings without strict comfort requirements.Examples are winter heating of warehouses and vacation homes,and heat drying of buildings under construction.We have set up a system that typically reduces electricity cost by about 40%on the basis of automatic weather and real time pricing forecasts.The system uses the building as an energy reservoir over periods with high electricity cost.Using a model predictive control system,we compare use of a genetic algorithm,a particle swarm optimization,and a neural network for heater control,all working in a closed loop to reduce the influence of modeling errors.We have simulated the performance of the systems using realistic data and found that all three optimizers give about the same performance,varying only a few percent in efficiency.However,the computational and memory requirements of the neural network are much lower than for the other optimizers,so it is preferable for use with inexpensive microcontrollers.We carried out a full-scale experiment at a residential house and found agreement with simulation results.展开更多
文摘Objective To investigate the prediction effect of neural networks for seismic response of structure under the Levenberg Marquardt(LM) algorithm. Results Based on identification and prediction ability of neural networks for nonlinear systems, and combined with LM algorithm, a multi layer forward networks is adopted to predict the seismic responses of structure. The networks is trained in batch by the shaking table test data of three floor reinforced concrete structure firstly, then the seismic responses of structure are predicted under the unused excitation data, and the predict responses are compared with the experiment responses. The error curves between the prediction and the experimental results show the efficiency of the method. Conclusion LM algorithm has very good convergence rate, and the neural networks can predict the seismic response of the structure well.
文摘Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters.Most machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box models.In addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source.These make using the ML models on new datasets difficult.This study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model.The data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for testing.Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best performance.The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases.Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP.Furthermore,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box model.Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.
文摘Considering recent developments in the energy sector,further reduction of electricity cost and flattening of the electric power demand curve are needed.We have focused on an autonomous electric heater control system that can easily be implemented in existing buildings without strict comfort requirements.Examples are winter heating of warehouses and vacation homes,and heat drying of buildings under construction.We have set up a system that typically reduces electricity cost by about 40%on the basis of automatic weather and real time pricing forecasts.The system uses the building as an energy reservoir over periods with high electricity cost.Using a model predictive control system,we compare use of a genetic algorithm,a particle swarm optimization,and a neural network for heater control,all working in a closed loop to reduce the influence of modeling errors.We have simulated the performance of the systems using realistic data and found that all three optimizers give about the same performance,varying only a few percent in efficiency.However,the computational and memory requirements of the neural network are much lower than for the other optimizers,so it is preferable for use with inexpensive microcontrollers.We carried out a full-scale experiment at a residential house and found agreement with simulation results.
