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Stochastic global maximum principle for optimization with recursive utilities 被引量:3
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作者 Mingshang Hu 《Probability, Uncertainty and Quantitative Risk》 2017年第1期1-20,共20页
In this paper,we study the recursive stochastic optimal control problems.The control domain does not need to be convex,and the generator of the backward stochastic differential equation can contain z.We obtain the var... In this paper,we study the recursive stochastic optimal control problems.The control domain does not need to be convex,and the generator of the backward stochastic differential equation can contain z.We obtain the variational equations for backward stochastic differential equations,and then obtain the maximum principle which solves completely Peng’s open problem. 展开更多
关键词 Backward stochastic differential equations Recursive stochastic optimal control Maximum principle Variational equation
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Optimal filtering for uncertain systems with stochastic nonlinearities, correlated noises and missing measurements 被引量:3
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作者 Shuo Zhang Yan Zhao +1 位作者 Min Li Jianhui Zhao 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2015年第5期1052-1059,共8页
The globally optimal recursive filtering problem is studied for a class of systems with random parameter matrices,stochastic nonlinearities, correlated noises and missing measurements. The stochastic nonlinearities ar... The globally optimal recursive filtering problem is studied for a class of systems with random parameter matrices,stochastic nonlinearities, correlated noises and missing measurements. The stochastic nonlinearities are presented in the system model to reflect multiplicative random disturbances, and the additive noises, process noise and measurement noise, are assumed to be one-step autocorrelated as well as two-step cross-correlated.A series of random variables is introduced as the missing rates governing the intermittent measurement losses caused by unfavorable network conditions. The aim of the addressed filtering problem is to design an optimal recursive filter for the uncertain systems based on an innovation approach such that the filtering error is globally minimized at each sampling time. A numerical simulation example is provided to illustrate the effectiveness and applicability of the proposed algorithm. 展开更多
关键词 globally optimal recursive filtering random parame- ter matrices stochastic nonlinearities correlated noises missing measurements
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Human motion prediction using optimized sliding window polynomial fitting and recursive least squares 被引量:2
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作者 Li Qinghua Zhang Zhao +3 位作者 Feng Chao Mu Yaqi You Yue Li Yanqiang 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2021年第3期76-85,110,共11页
Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid h... Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid human motion prediction algorithm,optimized sliding window polynomial fitting and recursive least squares(OSWPF-RLS)was proposed.The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input,and uses recursive least squares(RLS)to predict the human movement trajectories within the time window.Then,the optimized sliding window polynomial fitting(OSWPF)is used to calculate the multi-step prediction value,and the increment of multi-step prediction value was appropriately constrained.Experimental results show that compared with the existing benchmark algorithms,the OSWPF-RLS algorithm improved the multi-step prediction accuracy of human motion and enhanced the ability to respond to different human movements. 展开更多
关键词 human-robot collaboration(HRC) human motion prediction sliding window polynomial fitting(SWPF)algorithm recursive least squares(RLS) optimized sliding window polynomial fitting and recursive least squares(OSWPF-RLS)
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