The recent studies on Artificial Intelligence(AI)accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing...The recent studies on Artificial Intelligence(AI)accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adap-tiveness and fast responding features.The Model Predictive Control(MPC)tech-nique is a widely used,safe and reliable control method based on constraints.On the other hand,the Eddy Current dynamometers are highly nonlinear braking sys-tems whose performance parameters are related to many processes related vari-ables.This study is based on an adaptive model predictive control that utilizes selected AI methods.The presented approach presents an updated the mathema-tical model of an Eddy Current Dynamometer based on experimentally obtained system operational data.Finally,the comparison of AI methods and related learn-ing performances based on the assessment technique of mean absolute percentage error(MAPE)issues are discussed.The results indicate that Single Hidden Layer Neural Network(SHLNN),General Regression Neural Network(GRNN),Radial Basis Network(RBNN),Neuro Fuzzy Network(ANFIS)coupled MPC have quite satisfying performances.The presented results indicate that,amongst them,GRNN appears to provide the best performance.展开更多
Nowadays, one of the most important effects on water resources under climate change is increasing of free water surface evaporation which depends on the increasing of temperature. In basins, where there are no observe...Nowadays, one of the most important effects on water resources under climate change is increasing of free water surface evaporation which depends on the increasing of temperature. In basins, where there are no observed data, free water surface evaporation is taken into account depending on historical temperature and similar data and their long-term statistics. Predicting of real value of evaporation contains some uncertainties. The modeling of evaporation with a small number of predictors has crucial importance on the regions and basins where measurements are not sufficient and/or not exist. In this presented study, daily evaporation prediction models were prepared by using empirical Penman equation, Levenberg-Marquardt algorithm based on 'Feed Forward Back Propagation Artificial Neural Networks (LMANN)', radial basis neural networks (RBNN), generalized regression neural networks (GRNN). When the models were compared, it was noticed that the results of neural network models are statistically more meaningful than the Penman equation.展开更多
文摘The recent studies on Artificial Intelligence(AI)accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adap-tiveness and fast responding features.The Model Predictive Control(MPC)tech-nique is a widely used,safe and reliable control method based on constraints.On the other hand,the Eddy Current dynamometers are highly nonlinear braking sys-tems whose performance parameters are related to many processes related vari-ables.This study is based on an adaptive model predictive control that utilizes selected AI methods.The presented approach presents an updated the mathema-tical model of an Eddy Current Dynamometer based on experimentally obtained system operational data.Finally,the comparison of AI methods and related learn-ing performances based on the assessment technique of mean absolute percentage error(MAPE)issues are discussed.The results indicate that Single Hidden Layer Neural Network(SHLNN),General Regression Neural Network(GRNN),Radial Basis Network(RBNN),Neuro Fuzzy Network(ANFIS)coupled MPC have quite satisfying performances.The presented results indicate that,amongst them,GRNN appears to provide the best performance.
文摘Nowadays, one of the most important effects on water resources under climate change is increasing of free water surface evaporation which depends on the increasing of temperature. In basins, where there are no observed data, free water surface evaporation is taken into account depending on historical temperature and similar data and their long-term statistics. Predicting of real value of evaporation contains some uncertainties. The modeling of evaporation with a small number of predictors has crucial importance on the regions and basins where measurements are not sufficient and/or not exist. In this presented study, daily evaporation prediction models were prepared by using empirical Penman equation, Levenberg-Marquardt algorithm based on 'Feed Forward Back Propagation Artificial Neural Networks (LMANN)', radial basis neural networks (RBNN), generalized regression neural networks (GRNN). When the models were compared, it was noticed that the results of neural network models are statistically more meaningful than the Penman equation.
