Deep filtering
Deep filtering
复制标题
深度过滤
DOI：
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发表时间：
2020
影响因子：
0.9
通讯作者：
Qing Zhang
中科院分区：
文献类型：
--
作者：
L. Wang;G. Yin;Qing Zhang
本文提出了一种用于线性和非线性滤波的深度学习方法。这个想法是从一个标称的动态模型开始，然后生成蒙特卡罗样本路径。然后这些样本被用来训练一个深度神经网络。使用最小二乘误差作为网络训练的损失函数。然后将得到的权重应用于实际动态模型的蒙特卡罗样本。用这种方法得到的深度滤波器在线性情况下优于传统卡尔曼滤波器，在非线性情况下优于扩展卡尔曼滤波器。此外，还研究了一个带有跳变的切换模型，以证明我们的深度滤波方法的适应性和有效性。深度滤波的一个主要优点是当标称模型和实际模型不同时具有鲁棒性。深度滤波的另一个优点是可以直接使用真实数据来训练深度神经网络。因此，不需要校准模型。
This paper develops a deep learning method for linear and nonlinear filtering. The idea is to start with a nominal dynamic model and generate Monte Carlo sample paths. Then these samples are used to train a deep neutral network. A least square error is used as a loss function for network training. Then the resulting weights are applied to Monte Carlo sampl es from an actual dynamic model. The deep filter obtained in such a way compares favorably to the traditional Kalman filter in linear cases and the extended Kalman filter in nonlinear cases. Moreover, a switching model with jumps is studied to show the adaptiveness and power of our deep filtering method. A main advantage of deep filtering is its robustness when the nominal model and actual model differ. Another advantage of deep filtering is that real data can be used directly to train the deep neutral network. Therefore, one does not need to calibrate the model.