This paper presents state space methods for decentralized Hoe control, which contain two respects: a parametrization approach and an iterative algorithm. For large scale systems with N subsystems, decentralized Hoe c...This paper presents state space methods for decentralized Hoe control, which contain two respects: a parametrization approach and an iterative algorithm. For large scale systems with N subsystems, decentralized Hoe con trollers can be derived by a parametrization result for centralized Her: controllers and designed by an iterative algorithm with structured constraint to the controllers.展开更多
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
文摘This paper presents state space methods for decentralized Hoe control, which contain two respects: a parametrization approach and an iterative algorithm. For large scale systems with N subsystems, decentralized Hoe con trollers can be derived by a parametrization result for centralized Her: controllers and designed by an iterative algorithm with structured constraint to the controllers.
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.
