论文标题
深度过滤
Deep Filtering
论文作者
论文摘要
本文为线性和非线性过滤开发了一种深度学习方法。这个想法是从标称动态模型开始，然后生成蒙特卡洛样品路径。然后这些样品用于训练深中性网络。至少方误差用作网络训练的损失函数。然后将所得的权重应用于实际动态模型的蒙特卡洛采样。以这种方式获得的深滤波器与在线性案例中的传统卡尔曼滤波器和非线性情况下扩展的卡尔曼滤波器相比有利。此外，研究了带有跳跃的开关模型，以显示我们深层过滤方法的适应性和力量。 深度过滤的一个主要优点是当标称模型和实际模型不同时，其稳健性。深度过滤的另一个优点是，可以直接使用真实数据来训练深中性网络。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.
