论文标题
部分可观测时空混沌系统的无模型预测
Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees -- Technical Report
论文作者
论文摘要
复发性神经网络能够从输入输出测量中学习未知非线性系统的动力学。但是，所得模型在输入输出映射上没有提供任何稳定性保证。在这项工作中，我们代表一个反复的神经网络，是具有非线性干扰的线性时间不变系统。通过对参数引入约束，我们可以保证有限增益稳定性和增量有限增益稳定性。我们应用这种识别方法来学习四级自由船的运动，该船正在开放水中移动，并将其与其他纯粹的基于学习的方法进行比较。我们的分析表明，受约束的复发神经网络在测试集上具有较低的预测准确性，但在分布集合集合并尊重稳定性条件下，它取得了可比的结果。
Recurrent neural networks are capable of learning the dynamics of an unknown nonlinear system purely from input-output measurements. However, the resulting models do not provide any stability guarantees on the input-output mapping. In this work, we represent a recurrent neural network as a linear time-invariant system with nonlinear disturbances. By introducing constraints on the parameters, we can guarantee finite gain stability and incremental finite gain stability. We apply this identification method to learn the motion of a four-degrees-of-freedom ship that is moving in open water and compare it against other purely learning-based approaches with unconstrained parameters. Our analysis shows that the constrained recurrent neural network has a lower prediction accuracy on the test set, but it achieves comparable results on an out-of-distribution set and respects stability conditions.
