Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF...Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF） to train the MLP in a self- organizing state space （SOSS） model. This involves forming augmented state vectors consisting of all parameters （the weights of the MLP） and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS moder for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.展开更多
Bayesian estimation theory provides a general approach for the state estimate of linear or nonlinear and Gaussian or non-Gaussian systems. In this study, we first explore two Bayesian-based methods： ensemble adjustme...Bayesian estimation theory provides a general approach for the state estimate of linear or nonlinear and Gaussian or non-Gaussian systems. In this study, we first explore two Bayesian-based methods： ensemble adjustment Kalman filter（EAKF） and sequential importance resampling particle filter（SIR-PF）, using a well-known nonlinear and non-Gaussian model（Lorenz ＇63 model）. The EAKF, which is a deterministic scheme of the ensemble Kalman filter（En KF）, performs better than the classical（stochastic） En KF in a general framework. Comparison between the SIR-PF and the EAKF reveals that the former outperforms the latter if ensemble size is so large that can avoid the filter degeneracy, and vice versa. The impact of the probability density functions and effective ensemble sizes on assimilation performances are also explored. On the basis of comparisons between the SIR-PF and the EAKF, a mixture filter, called ensemble adjustment Kalman particle filter（EAKPF）, is proposed to combine their both merits. Similar to the ensemble Kalman particle filter, which combines the stochastic En KF and SIR-PF analysis schemes with a tuning parameter, the new mixture filter essentially provides a continuous interpolation between the EAKF and SIR-PF. The same Lorenz ＇63 model is used as a testbed, showing that the EAKPF is able to overcome filter degeneracy while maintaining the non-Gaussian nature, and performs better than the EAKF given limited ensemble size.展开更多
It is well acknowledged to all that an active equalization strategy can overcome the inconsistency of lithium-ion cell's voltage and state of charge(SOC)in series-connected lithium-ion battery(LIB)pack in the elec...It is well acknowledged to all that an active equalization strategy can overcome the inconsistency of lithium-ion cell's voltage and state of charge(SOC)in series-connected lithium-ion battery(LIB)pack in the electric vehicle application.In this regard,a novel dual threshold trigger mechanism based active equalization strategy(DTTMbased AES)is proposed to overcome the inherent inconsistency of cells and to improve the equalization efficiency for a series-connected LIB pack.First,a modified dual-layer inductor equalization circuit is constructed to make it possible for the energy transfer path optimization.Next,based on the designed dual threshold trigger mechanism provoked by battery voltage and SOC,an active equalization strategy is proposed,each single cell's SOC in the battery packs is estimated using the extended Kalman particle filter algorithm.Besides,on the basis of the modified equalization circuit,the improved particle swarm optimization is adopted to optimize the energy transfer path with aiming to reduce the equalization time.Lastly,the simulation and experimental results are provided to validate the proposed DTTM-based AES.展开更多
基金supported by the National Natural Science Foundation of China(7092100160574058)+1 种基金the Key International Cooperation Programs of Hunan Provincial Science & Technology Department (2009WK2009)the General Program of Hunan Provincial Education Department(11C0023)
文摘Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF） to train the MLP in a self- organizing state space （SOSS） model. This involves forming augmented state vectors consisting of all parameters （the weights of the MLP） and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS moder for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.
基金The National Natural Science Foundation of China under contract Nos 41276029 and 41321004the Project of State Key Laboratory of Satellite Ocean Environment Dynamics,Second Institute of Oceanography under contract No.SOEDZZ1404the National Basic Research Program(973 Program)of China under contract No.2013CB430302
文摘Bayesian estimation theory provides a general approach for the state estimate of linear or nonlinear and Gaussian or non-Gaussian systems. In this study, we first explore two Bayesian-based methods： ensemble adjustment Kalman filter（EAKF） and sequential importance resampling particle filter（SIR-PF）, using a well-known nonlinear and non-Gaussian model（Lorenz ＇63 model）. The EAKF, which is a deterministic scheme of the ensemble Kalman filter（En KF）, performs better than the classical（stochastic） En KF in a general framework. Comparison between the SIR-PF and the EAKF reveals that the former outperforms the latter if ensemble size is so large that can avoid the filter degeneracy, and vice versa. The impact of the probability density functions and effective ensemble sizes on assimilation performances are also explored. On the basis of comparisons between the SIR-PF and the EAKF, a mixture filter, called ensemble adjustment Kalman particle filter（EAKPF）, is proposed to combine their both merits. Similar to the ensemble Kalman particle filter, which combines the stochastic En KF and SIR-PF analysis schemes with a tuning parameter, the new mixture filter essentially provides a continuous interpolation between the EAKF and SIR-PF. The same Lorenz ＇63 model is used as a testbed, showing that the EAKPF is able to overcome filter degeneracy while maintaining the non-Gaussian nature, and performs better than the EAKF given limited ensemble size.
基金supported by the Artificial intelligence technology project of Xi'an Science and Technology Bureau(No.21RGZN0014).
文摘It is well acknowledged to all that an active equalization strategy can overcome the inconsistency of lithium-ion cell's voltage and state of charge(SOC)in series-connected lithium-ion battery(LIB)pack in the electric vehicle application.In this regard,a novel dual threshold trigger mechanism based active equalization strategy(DTTMbased AES)is proposed to overcome the inherent inconsistency of cells and to improve the equalization efficiency for a series-connected LIB pack.First,a modified dual-layer inductor equalization circuit is constructed to make it possible for the energy transfer path optimization.Next,based on the designed dual threshold trigger mechanism provoked by battery voltage and SOC,an active equalization strategy is proposed,each single cell's SOC in the battery packs is estimated using the extended Kalman particle filter algorithm.Besides,on the basis of the modified equalization circuit,the improved particle swarm optimization is adopted to optimize the energy transfer path with aiming to reduce the equalization time.Lastly,the simulation and experimental results are provided to validate the proposed DTTM-based AES.
