This paper focuses on fixed-interval smoothing for stochastic hybrid systems.When the truth-mode mismatch is encountered,existing smoothing methods based on fixed structure of model-set have significant performance de...This paper focuses on fixed-interval smoothing for stochastic hybrid systems.When the truth-mode mismatch is encountered,existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable.We develop a fixedinterval smoothing method based on forward-and backward-filtering in the Variable Structure Multiple Model(VSMM)framework in this paper.We propose to use the Simplified Equivalent model Interacting Multiple Model(SEIMM)in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters,and design a re-filtering procedure in the model-switching stage to enhance the estimation performance.To improve the computational efficiency,we make the basic model-set adaptive by the Likely-Model Set(LMS)algorithm.It turns out that the smoothing performance is further improved by the LMS due to less competition among models.Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.展开更多
作为一种广为接受的语义数据模型,E-R模型被广泛地应用于数据库设计阶段.但是E-R模型自身却存在某些缺陷,这些缺陷制约了对其进一步的应用.针对E-R模型的改进,目前主要存在基于图形表示和描述性逻辑表示两种途径.但是,前者仍然不具有自...作为一种广为接受的语义数据模型,E-R模型被广泛地应用于数据库设计阶段.但是E-R模型自身却存在某些缺陷,这些缺陷制约了对其进一步的应用.针对E-R模型的改进,目前主要存在基于图形表示和描述性逻辑表示两种途径.但是,前者仍然不具有自动推理能力,而后者却存在表示能力弱、与数据库兼容性不足等缺陷.为克服以上缺陷,提出一种利用回答集编程(answer set programming)表示E-R模型的新方法.首先,对应于数据库的E-R模式被区分为基本和扩展两种类型,并分别完成它们的语法与语义定义.其次,利用回答集编程完成以上两类模式的逻辑编程表示.最后,完成表示的正确性证明.提出的方法不仅为E-R模型提供了一种新的逻辑表示途径,而且相对原有的两种E-R模型改进途径具有明显的优势.更为重要的是该研究成果使得应用E-R模型实现异构数据库之间的语义协作成为可能.展开更多
The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is ...The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.展开更多
基金supported in part by the National Natural Science Foundation of China(No.61773306)the National Key Research and Development Plan,China(Nos.2021YFC2202600 and 2021YFC2202603)。
文摘This paper focuses on fixed-interval smoothing for stochastic hybrid systems.When the truth-mode mismatch is encountered,existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable.We develop a fixedinterval smoothing method based on forward-and backward-filtering in the Variable Structure Multiple Model(VSMM)framework in this paper.We propose to use the Simplified Equivalent model Interacting Multiple Model(SEIMM)in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters,and design a re-filtering procedure in the model-switching stage to enhance the estimation performance.To improve the computational efficiency,we make the basic model-set adaptive by the Likely-Model Set(LMS)algorithm.It turns out that the smoothing performance is further improved by the LMS due to less competition among models.Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.
文摘作为一种广为接受的语义数据模型,E-R模型被广泛地应用于数据库设计阶段.但是E-R模型自身却存在某些缺陷,这些缺陷制约了对其进一步的应用.针对E-R模型的改进,目前主要存在基于图形表示和描述性逻辑表示两种途径.但是,前者仍然不具有自动推理能力,而后者却存在表示能力弱、与数据库兼容性不足等缺陷.为克服以上缺陷,提出一种利用回答集编程(answer set programming)表示E-R模型的新方法.首先,对应于数据库的E-R模式被区分为基本和扩展两种类型,并分别完成它们的语法与语义定义.其次,利用回答集编程完成以上两类模式的逻辑编程表示.最后,完成表示的正确性证明.提出的方法不仅为E-R模型提供了一种新的逻辑表示途径,而且相对原有的两种E-R模型改进途径具有明显的优势.更为重要的是该研究成果使得应用E-R模型实现异构数据库之间的语义协作成为可能.
基金supported in part by National Basic Research Program of China(No.2012CB821200)in part by the National Natural Science Foundation of China(No.61174024)
文摘The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.
