In this work, we introduce and study a class of Deep Neural Networks (DNNs)
in continuous-time. The proposed architecture stems from the combination of
Neural Ordinary Differential Equations (Neural ODEs) with the model structure
of recently introduced Recurrent Equilibrium Networks (RENs). We show how to
endow our proposed NodeRENs with contractivity and dissipativity -- crucial
properties for robust learning and control. Most importantly, as for RENs, we
derive parametrizations of contractive and dissipative NodeRENs which are
unconstrained, hence enabling their learning for a large number of parameters.
We validate the properties of NodeRENs, including the possibility of handling
irregularly sampled data, in a case study in nonlinear system identification.

该论文介绍了一种使用神经常微分方程与循环平衡网络相结合的连续时间深度神经网络，该网络在不受限制的参数化情况下拥有可收缩和可耗散性，且可以处理非规则采样数据，用于非线性系统识别。