PARTIALLY OBSERVABLE STOCHASTIC OPTIMAL CONTROL

被引:0
|
作者
Wang, Guangchen [1 ]
Xiong, Jie [2 ,3 ]
Zhang, Shuaiqi [4 ]
机构
[1] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China
[2] Univ Macau, Dept Math, Taipa Macau, Peoples R China
[3] Univ Tennessee, Dept Math, Knoxville, TN 37996 USA
[4] Guangdong Univ Technol, Dept Econ, Guangzhou 510520, Guangdong, Peoples R China
关键词
Branching particle system; forward-backward stochastic differential equation; numerical approximation; maximum principle; stochastic filtering; MAXIMUM PRINCIPLE; PARTICLE APPROXIMATION; FILTERING EQUATIONS; SYSTEMS; CONVERGENCE; STATE;
D O I
暂无
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
This paper is survey on some recent results in optimal control and stochastic filtering. The goal is not to cover all recent developments in control and filtering, instead we focus on maximum principle for optimality of partial information backward or forward-backward stochastic differential equations and branching particle approximation of nonlinear filtering.
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页码:493 / 512
页数:20
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