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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Multistep Prediction of Dynamic Systems With Recurrent Neural Networks
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Multistep Prediction of Dynamic Systems With Recurrent Neural Networks

机译：递归神经网络的动态系统多步预测

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摘要

In this paper, we address the state initialization problem in recurrent neural networks (RNNs), which seeks proper values for the RNN initial states at the beginning of a prediction interval. The proposed methods employ various forms of neural networks (NNs) to generate proper initial state values for RNNs. A variety of RNNs are trained using the proposed NN initialization schemes for modeling two aerial vehicles, a helicopter and a quadrotor, from experimental data. It is shown that the RNN initialized by the NN-based initialization method outperforms the washout method which is commonly used to initialize RNNs. Furthermore, a comprehensive study of RNNs trained for multistep prediction of the two aerial vehicles is presented. The multistep prediction of the quadrotor is enhanced using a hybrid model, which combines a simplified physics-based motion model of the vehicle with RNNs. While the maximum translational and rotational velocities in the Quadrotor data set are about 4 m/s and 3.8 rad/s, respectively, the hybrid model produces predictions, over 1.9 s, which remain within 9 cm/s and 0.12 rad/s of the measured translational and rotational velocities, with 99 & x0025; confidence on the test data set.
机译：在本文中，我们解决了循环神经网络（RNN）中的状态初始化问题，该问题在预测间隔开始时为RNN初始状态寻求适当的值。所提出的方法采用各种形式的神经网络（NN）来生成RNN的适当初始状态值。使用建议的NN初始化方案对各种RNN进行训练，以根据实验数据对两架飞行器，直升机和四旋翼飞机进行建模。结果表明，通过基于NN的初始化方法初始化的RNN优于通常用于初始化RNN的冲洗方法。此外，还介绍了经过训练可对两架飞行器进行多步预测的RNN。使用混合模型增强了四旋翼飞机的多步预测，该模型将车辆的基于物理学的简化运动模型与RNN相结合。尽管Quadrotor数据集中的最大平移速度和旋转速度分别约为4 m / s和3.8 rad / s，但混合模型产生的预测值超过1.9 s，仍保持在9 cc / s和0.12 rad / s的范围内。测得的平移和旋转速度为99＆x0025;对测试数据集的信心。

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