时变高斯过程假设密度滤波算法
Time-Varying Gaussian Process Assume Density Filter Algorithm
高斯过程是一种有效的数据驱动建模方法, 已应用于解决时不变动态系统的状态估计问题. 为了提升高斯过程动态系统的自适应能力, 文章对参数时变的高斯过程动态系统, 通过粒子滤波算法实时更新参数, 将更新后的参数代入到高 斯过程假设密度滤波算法得到时变高斯过程假设密度滤波算法. 数值例子结果表明时变高斯过程假设密度算法的有效性.
Gaussian process is an effective data-driven modeling method, which has been applied to solve the state estimation problem of time-invariant dynamic systems. In this paper, in order to improve the adaptive ability of the gaussian process dynamic system, the timer-varying dynamic system is considered, and the parameters are updated by the particle filter algorithm. The updated parameters are substituted into the Gaussian process assumed density filter algorithm to obtain the time-varying Gaussian process assumed density filter algorithm. A simulation example verifies the effectiveness of the proposed algorithms.
高斯过程 / 动态系统 / 粒子滤波 / 时变高斯过程假设密度滤波. {{custom_keyword}} /
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