一个广义隐马尔可夫模型的辅助双滤波粒子平滑法

IF 6.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yunqi Chen , Zhibin Yan , Xing Zhang
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引用次数: 0

摘要

本文针对一个广义隐马尔可夫模型(GHMM)的非线性定间隔平滑问题,提出了双滤波粒子平滑(TFPS)算法,其中当前观测值不仅取决于当前状态,还取决于一步前状态。首先,通过贝叶斯方法,建立了 GHMM 的双滤波平滑(TFS)公式来计算平滑密度。在这个 TFS 公式中,后向信息预测密度一般不是状态密度。这就造成了一个难题,即不能直接应用普通的序列蒙特卡罗(SMC)采样技术来设计基于 TFS 公式的相应 TFPS 算法。为解决这一难题,本文通过引入人工密度序列,提出了针对 GHMM 的广义 TFS 公式。结合广义 TFS 公式、SMC 和辅助变量采样技术,提出了计算复杂度为二次方的基本辅助 TFPS(ATFPS)算法,并进一步发展了计算复杂度为线性的简化 ATFPS 算法。最后,通过仿真实例和实际实验数据验证了所提出的两种 ATFPS 算法在 GHMM 中的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Auxiliary two-filter particle smoothing for one generalized hidden Markov model

This paper develops two-filter particle smoothing (TFPS) algorithms for the nonlinear fixed-interval smoothing problem of one generalized hidden Markov model (GHMM), where the current observation depends not only on the current state, but also on one-step previous state. Firstly, by Bayesian approach, the two-filter smoothing (TFS) formula for GHMM is established to calculate smoothing densities. In this TFS formula, the backward information prediction density is generally not a density of the state. This results in a difficulty that the normal sequential Monte Carlo (SMC) sampling technique cannot be directly applied to design corresponding TFPS algorithms based on the TFS formula. To solve this difficulty, a generalized TFS formula for GHMM is then proposed by introducing a sequence of artificial densities. By combining this generalized TFS formula, SMC, and the auxiliary variable sampling technique, a basic auxiliary TFPS (ATFPS) algorithm with quadratic computational complexity is proposed, and a simplified ATFPS algorithm with linear computational complexity is further developed. Finally, the effectiveness and superiority of the two proposed ATFPS algorithms for GHMM are verified via simulation examples and real experimental data.

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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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