Particle filters for partially-observed Boolean dynamical systems
Particle filters for partially-observed Boolean dynamical systems
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DOI：
10.1016/j.automatica.2017.10.009
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发表时间：
2018-01-01
期刊：
影响因子：
6.4
通讯作者：
Braga-Neto, Ulisses M.
中科院分区：
文献类型：
--
作者：
Imani, Mandi;Braga-Neto, Ulisses M.
部分可观测布尔动力系统 (POBDS) 是一类通用的非线性模型，可用于基于噪声和不完整测量的布尔过程的估计和控制。 POBDS 状态估计的最佳最小均方误差 (MMSE) 算法，即布尔卡尔曼滤波器 (BKF) 和布尔卡尔曼平滑器 (BKS)，由于计算和内存要求，在大型系统的情况下很难处理。为了解决这个问题，我们引入了基于辅助粒子滤波器（APF）方法的近似MMSE滤波和平滑算法，分别称为APF-BKF和APF-BKS。对于联合状态和参数估计，APF-BKF 与最大似然 (ML) 方法联合使用，在 POBDS 模型中同时进行状态和参数估计。在未知参数是离散的情况下，所提出的 ML 自适应滤波器由多个并行运行的 APF-BKF 组成，其方式让人想起经典线性滤波理论中的多模型自适应估计（MMAE）方法。在存在连续参数的情况下，所提出的 ML 自适应滤波器基于 POBDS 模型的有效的基于粒子的期望最大化 (EM) 算法，该算法基于改进的前向滤波器后向模拟 (FFBSi) 与 APF-BKS 的结合。所提出的基于粒子的自适应滤波器的性能通过数值实验进行评估，使用通过噪声 RNA-Seq 时间序列数据观察到的众所周知的细胞周期基因调控网络的 POBDS 模型。 (C) 2017 Elsevier Ltd. 保留所有权利。
Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear models with application in estimation and control of Boolean processes based on noisy and incomplete measurements. The optimal minimum mean square error (MMSE) algorithms for POBDS state estimation, namely, the Boolean Kalman filter (BKF) and Boolean Kalman smoother (BKS), are intractable in the case of large systems, due to computational and memory requirements. To address this, we introduce approximate MMSE filtering and smoothing algorithms based on the auxiliary particle filter (APF) method, which are called APF-BKF and APF-BKS, respectively. For joint state and parameter estimation, the APF-BKF is used jointly with maximum-likelihood (ML) methods for simultaneous state and parameter estimation in POBDS models. In the case the unknown parameters are discrete, the proposed ML adaptive filter consists of multiple APF-BKFs running in parallel, in a manner reminiscent of the Multiple Model Adaptive Estimation (MMAE) method in classical linear filtering theory. In the presence of continuous parameters, the proposed ML adaptive filter is based on an efficient particle-based expectation maximization (EM) algorithm for the POBDS model, which is based on a modified Forward Filter Backward Simulation (FFBSi) in combination with the APF-BKS. The performance of the proposed particle-based adaptive filters is assessed through numerical experiments using a POBDS model of the well-known cell cycle gene regulatory network observed through noisy RNA-Seq time series data. (C) 2017 Elsevier Ltd. All rights reserved.