基于更新过程噪声协方差的改进Rao-Blackwellised粒子滤波用于化学反应网络
A Modified Rao-Blackwellised Particle Filter Based on Updated Process Noise Covariance for Chemical Reaction Networks
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中文总结 AI 辅助
本文针对化学反应网络，提出一种基于递归更新过程噪声协方差的改进Rao-Blackwellised粒子滤波，联合估计参数与状态，并在精度、误差和白度间取得平衡。
中文摘要 AI 辅助
准确表征化学反应网络（CRNs）对于促进合成和自然发生的生物系统的工程化是必要的。在CRNs中估计多个未知参数和状态需要首先进行可辨识性分析，然后采用合适的估计技术。在本文中，我们以一个降阶基因表达系统为例，对多个未知参数进行了参数敏感性分析，并使用改进的Rao-Blackwellised粒子滤波（RBPF）来估计参数和状态。所提出的框架使用粒子滤波估计参数，使用扩展卡尔曼滤波（EKF）估计状态，其中过程噪声协方差基于化学朗之万方程（CLE）递归更新。我们将所提出的滤波器与固定噪声协方差选择在参数估计精度、状态估计误差和innovation序列白度方面进行了比较。我们发现，具有更新噪声协方差的RBPF在这三个标准之间找到了平衡，证明了其适用于随机CRNs的联合状态和参数估计。
英文摘要
It is necessary to characterize chemical reaction networks (CRNs) accurately to facilitate engineering of both synthetic and naturally occurring biological systems. Estimation of multiple unknown parameters and states in CRNs requires first, an identifiability analysis, and then adoption of a suitable estimation technique. In this paper, we took an example of a reduced order gene expression system, performed parameter sensitivity analysis for multiple unknown parameters, and used a modified Rao-Blackwellised particle filter (RBPF) to estimate parameters and states. The proposed framework estimates parameters using a particle filter and states using an extended Kalman filter (EKF) with process noise covariance updated recursively based on chemical Langevin equation (CLE). We compared the accuracy of parameter estimation, error in state estimation, and whiteness of the innovation sequence for the proposed filter with fixed choices of noise covariance. We found that the RBPF with updated noise covariance finds a balance between these three criteria, demonstrating its suitability for joint state and parameter estimation for stochastic CRNs.
发表机构
- National Institute of Technology, Rourkela（印度理工学院鲁尔基分校）
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