提出可微分的多模型粒子滤波算法，同时学习系统行为模式与跳变规律。
Differentiable Interacting Multiple Model Particle Filtering
- 设计可微分的交互多模型粒子滤波器，联合学习行为模式与跳变机制。
- 新梯度估计器方差更低且计算快，理论证明一致，提升训练稳定性。
- 支持按模式分配计算资源，适合高维参数如神经网络的在线学习。
针对具有随机间断行为的模型，我们提出一种序列蒙特卡洛算法用于参数学习。为高效处理高维参数（如神经网络参数），采用可微分粒子滤波框架，通过梯度下降训练参数。设计新型可微分交互多模型粒子滤波器，可同时学习各行为模式及控制跳变的模型。相比已有方法，该算法可在不同模式间灵活分配计算量，并利用模式概率指导采样。此外，提出的新梯度估计器方差更低、计算高效，且理论证明一致。我们建立了算法的新理论结果，并在数值实验中展现出优于现有最先进方法的性能。
原文摘要 · Abstract (English)
We propose a sequential Monte Carlo algorithm for parameter learning when the studied model exhibits random discontinuous jumps in behaviour. To facilitate the learning of high dimensional parameter sets, such as those associated to neural networks, we adopt the emerging framework of differentiable particle filtering, wherein parameters are trained by gradient descent. We design a new differentiable interacting multiple model particle filter to be capable of learning the individual behavioural regimes and the model which controls the jumping simultaneously. In contrast to previous approaches, our algorithm allows control of the computational effort assigned per regime whilst using the probability of being in a given regime to guide sampling. Furthermore, we develop a new gradient estimator that has a lower variance than established approaches and remains fast to compute, for which we prove consistency. We establish new theoretical results of the presented algorithms and demonstrate superior numerical performance compared to the previous state-of-the-art algorithms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。