Learning Hybrid Models with Guarded Transitions
Learning Hybrid Models with Guarded Transitions
复制标题
学习具有保护转换的混合模型
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
C. Forster
中科院分区：
文献类型：
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作者：
P. Santana;Spencer Lane;E. Timmons;B. Williams;C. Forster
已经开发了用于诊断，活动监测和状态估计的创新方法，这些方法通过使用涉及混合离散和连续行为的随机模型来实现高精度。一个关键的瓶颈是对这些混合模型的自动采集，最近的方法主要集中在跳跃马尔可夫流程和分段自回归模型上。在本文中，我们提出了一种新型算法，该算法能够对受保护的概率混合自动机（PHA）模型进行无监督的学习，该模型通过允许在混合系统中的随机离散模式过渡来扩展先前的工作，从而对其连续状态具有功能依赖性。我们的实验表明，在混合状态估计器使用时，受保护的PHA模型可以产生显着的性能改进，尤其是在诊断系统的真实离散模式时，而不会对其实时性能产生任何明显影响。
Innovative methods have been developed for diagnosis, activity monitoring, and state estimation that achieve high accuracy through the use of stochastic models involving hybrid discrete and continuous behaviors. A key bottleneck is the automated acquisition of these hybrid models, and recent methods have focused predominantly on Jump Markov processes and piecewise autoregressive models. In this paper, we present a novel algorithm capable of performing unsupervised learning of guarded Probabilistic Hybrid Automata (PHA) models, which extends prior work by allowing stochastic discrete mode transitions in a hybrid system to have a functional dependence on its continuous state. Our experiments indicate that guarded PHA models can yield significant performance improvements when used by hybrid state estimators, particularly when diagnosing the true discrete mode of the system, without any noticeable impact on their real-time performance.