Minimum entropy filtering for multivariate stochastic systems with non-Gaussian noises

CASIA OpenIR > 09年以前成果

	Minimum entropy filtering for multivariate stochastic systems with non-Gaussian noises; Minimum entropy filtering for multivariate stochastic systems with non-Gaussian noises; Minimum entropy filtering for multivariate stochastic systems with non-Gaussian noises
	Guo, L ; Wang, H
发表期刊	IEEE TRANSACTIONS ON AUTOMATIC CONTROL ; IEEE TRANSACTIONS ON AUTOMATIC CONTROL ; IEEE TRANSACTIONS ON AUTOMATIC CONTROL
	2006-04-01 ; 2006-04-01 ; 2006-04-01
卷号	51 期号:4 页码:695-700
文章类型	Article ; Article ; Article
摘要	In this note, a minimum entropy filtering algorithm is presented for a class of multivariate dynamic stochastic systems, which are represented by a set of time-varying difference equations and are subjected to the multivariate non-Gaussian stochastic inputs. Several new concepts including the hybrid random vector, hybrid probability and hybrid entropy are firstly established to describe the probabilistic property of the estimation errors. New relationships are provided between the probability density functions (PDF's) of the multivariate stochastic input and output for different mapping cases. Recursive algorithms are then proposed to design the real-time sub-optimal filter so that the hybrid entropy of the estimation error can be minimized. Finally, an improved algorithm is provided through the on-line tuning of the weighting matrices so as to guarantee the local stability of the error system.; In this note, a minimum entropy filtering algorithm is presented for a class of multivariate dynamic stochastic systems, which are represented by a set of time-varying difference equations and are subjected to the multivariate non-Gaussian stochastic inputs. Several new concepts including the hybrid random vector, hybrid probability and hybrid entropy are firstly established to describe the probabilistic property of the estimation errors. New relationships are provided between the probability density functions (PDF's) of the multivariate stochastic input and output for different mapping cases. Recursive algorithms are then proposed to design the real-time sub-optimal filter so that the hybrid entropy of the estimation error can be minimized. Finally, an improved algorithm is provided through the on-line tuning of the weighting matrices so as to guarantee the local stability of the error system.; In this note, a minimum entropy filtering algorithm is presented for a class of multivariate dynamic stochastic systems, which are represented by a set of time-varying difference equations and are subjected to the multivariate non-Gaussian stochastic inputs. Several new concepts including the hybrid random vector, hybrid probability and hybrid entropy are firstly established to describe the probabilistic property of the estimation errors. New relationships are provided between the probability density functions (PDF's) of the multivariate stochastic input and output for different mapping cases. Recursive algorithms are then proposed to design the real-time sub-optimal filter so that the hybrid entropy of the estimation error can be minimized. Finally, an improved algorithm is provided through the on-line tuning of the weighting matrices so as to guarantee the local stability of the error system.
关键词	Entropy Optimization Entropy Optimization Entropy Optimization Hybrid Probability Hybrid Probability Hybrid Probability Non-gaussian Systems Non-gaussian Systems Non-gaussian Systems Nonlinear Systems Nonlinear Systems Nonlinear Systems Stochastic Filtering Stochastic Filtering Stochastic Filtering
WOS标题词	Science & Technology ; Science & Technology ; Science & Technology ; Technology ; Technology ; Technology
收录类别	SCI ; SCI ; SCI
语种	英语 ; 英语 ; 英语
WOS研究方向	Automation & Control Systems ; Automation & Control Systems ; Automation & Control Systems ; Engineering ; Engineering ; Engineering
WOS类目	Automation & Control Systems ; Automation & Control Systems ; Automation & Control Systems ; Engineering, Electrical & Electronic ; Engineering, Electrical & Electronic ; Engineering, Electrical & Electronic
WOS记录号	WOS:000236775600021 ; WOS:000236775600021 ; WOS:000236775600021
引用统计	被引频次：92[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/9351
专题	09年以前成果
作者单位	1.SE Univ, Res Inst Automat, Nanjing 210096, Peoples R China 2.Univ Manchester, Control Syst Ctr, Manchester M60 1QD, Lancs, England 3.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Guo, L,Wang, H. Minimum entropy filtering for multivariate stochastic systems with non-Gaussian noises, Minimum entropy filtering for multivariate stochastic systems with non-Gaussian noises, Minimum entropy filtering for multivariate stochastic systems with non-Gaussian noises[J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, IEEE TRANSACTIONS ON AUTOMATIC CONTROL,2006, 2006, 2006,51, 51, 51(4):695-700, 695-700, 695-700.
APA	Guo, L,&Wang, H.(2006).Minimum entropy filtering for multivariate stochastic systems with non-Gaussian noises.IEEE TRANSACTIONS ON AUTOMATIC CONTROL,51(4),695-700.
MLA	Guo, L,et al."Minimum entropy filtering for multivariate stochastic systems with non-Gaussian noises".IEEE TRANSACTIONS ON AUTOMATIC CONTROL 51.4(2006):695-700.