一类随机动态过程基于q阶树的多尺度建模方法
A qth-order Tree-based Method for Multiscale Modeling of Stochastic Dynamic Processes
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摘要: 利用多尺度随机模型能建立处理问题有效并行算法的这一优势,提出一类随机动态过程基于一般q阶树的多尺度建模方法。首先,利用Markov过程的条件独立性给出一类过程基于q阶树的多尺度表示方法;其次,基于q阶树多尺度表示和具体实例推导出多尺度模型中的状态转移矩阵、扰动阵、初始状态和相应的协方差矩阵等的具体形式,为具有Markov统计特性的过程或信号建立起多尺度随机模型,这将为有效地解决多源同类信息和多源异类信息的数据融合等实际问题提供了理论基础;最后,给出一类Gauss-Markov过程基于三阶树和五阶树多尺度表示的计算机仿真结果,进一步验证建立模型的实用性和有效性。Abstract: In this paper, by using the advantage of an extremely efficient and highly parallelizable algorithm deriving from the multiscaie stochastic model to deal with a lot of practical problem, a general qth-order tree-based method for multiscale modeling of stochastic dynamic processes is developed. Firstly, using the property of conditional independence of Markov processes, a qth-order tree-based method for multiscale representation of a class of process is presented. Secondly, the representation forms of the parameters in the model, such as the state transition matrix, the disturbance matrix, the initial state and the corresponding covariance are deduced by example in detail based on
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