In this paper, a back propagation artificial neural network (BP-ANN) model is presented for the simultaneous estimation of vapour liquid equilibria (VLE) of four binary systems viz chlorodifluoromethan-carbondioxi...In this paper, a back propagation artificial neural network (BP-ANN) model is presented for the simultaneous estimation of vapour liquid equilibria (VLE) of four binary systems viz chlorodifluoromethan-carbondioxide, trifluoromethan-carbondioxide, carbondisulfied-trifluoromethan and carbondisulfied-chlorodifluoromethan. VLE data of the systems were taken from the literature for wide ranges of temperature (222.04-343.23K) and pressure (0.105 to 7.46MPa). BP-ANN trained by the Levenberg-Marquardt algorithm in the MATLAB neural network toolbox was used for building and optimizing the model. It is shown that the established model could estimate the VLE with satisfactory precision and accuracy for the four systems with the root mean square error in the range of 0.054-0.119. Predictions using BP-ANN were compared with the conventional Redlich-Kwang-Soave (RKS) equation of state, suggesting that BP-ANN has better ability in estimation as compared with the RKS equation (the root mean square error in the range of 0.115-0.1546).展开更多
Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algori...Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algorithms and particle filtering algorithm.The weighted algorithms have good realtime property,however have poor estimation property and some of them does not suit for target’s variable velocity model.The particle filtering algorithm can estimate target's position more accurately with poor realtime property and is not suitable for target’s constant velocity model.In this paper distance weight is adopted to estimate the target’s position,which is different from the existing distance weight in other papers.On the analysis of principle of distance weight (DW),prediction-based distance weighted(PDW) algorithm for target tracking in BSN is proposed.Simulation results proved PDW fits for target's constant and variable velocity models with accurate estimation and good realtime property.展开更多
文摘In this paper, a back propagation artificial neural network (BP-ANN) model is presented for the simultaneous estimation of vapour liquid equilibria (VLE) of four binary systems viz chlorodifluoromethan-carbondioxide, trifluoromethan-carbondioxide, carbondisulfied-trifluoromethan and carbondisulfied-chlorodifluoromethan. VLE data of the systems were taken from the literature for wide ranges of temperature (222.04-343.23K) and pressure (0.105 to 7.46MPa). BP-ANN trained by the Levenberg-Marquardt algorithm in the MATLAB neural network toolbox was used for building and optimizing the model. It is shown that the established model could estimate the VLE with satisfactory precision and accuracy for the four systems with the root mean square error in the range of 0.054-0.119. Predictions using BP-ANN were compared with the conventional Redlich-Kwang-Soave (RKS) equation of state, suggesting that BP-ANN has better ability in estimation as compared with the RKS equation (the root mean square error in the range of 0.115-0.1546).
基金This work is supported by The National Science Fund for Distinguished Young Scholars (60725105) National Basic Research Program of China (973 Program) (2009CB320404)+5 种基金 Program for Changjiang Scholars and Innovative Research Team in University (IRT0852) The National Natural Science Foundation of China (60972048, 61072068) The Special Fund of State Key Laboratory (ISN01080301) The Major program of National Science and Technology (2009ZX03007- 004) Supported by the 111 Project (B08038) The Key Project of Chinese Ministry of Education (107103).
文摘Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algorithms and particle filtering algorithm.The weighted algorithms have good realtime property,however have poor estimation property and some of them does not suit for target’s variable velocity model.The particle filtering algorithm can estimate target's position more accurately with poor realtime property and is not suitable for target’s constant velocity model.In this paper distance weight is adopted to estimate the target’s position,which is different from the existing distance weight in other papers.On the analysis of principle of distance weight (DW),prediction-based distance weighted(PDW) algorithm for target tracking in BSN is proposed.Simulation results proved PDW fits for target's constant and variable velocity models with accurate estimation and good realtime property.