模块化深度循环神经网络：在四旋翼飞行器中的应用
Modular Deep Recurrent Neural Network: Application to Quadrotors
- University of Waterloo（滑铁卢大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出模块化深度RNN，通过加入层间前馈连接提升RNN学习高阶动力学的能力并缓解梯度问题，经四旋翼案例验证其泛化速度优于现有方法。
AI中文摘要：
本文提出一种模块化深度循环神经网络（RNN），以简化各类RNN架构的部署流程，并为基于梯度的学习方法自动计算导数。该模块化设计催生了一系列新架构，其中一种包含层间前馈连接。在多层RNN中加入层间前馈连接后，RNN学习和建模高阶动力学与非线性的能力显著提升，同时也缓解了多层RNN在空间维度上的梯度消失/爆炸问题。四旋翼飞行器的案例研究验证了这些结果：采用该特定网络结构学习高度动力学模型时，现有方法无法实现如此快速的泛化，甚至完全无法泛化。
英文摘要：
A modular deep Recurrent Neural Network (RNN) is introduced to facilitate the process of deploying various architectures of RNNs, and to automatically compute derivatives for gradient-based learning methods. The modularity leads to a set of new architectures, one of which includes feedforward inter-layer connections. By adding feedforward inter-layer connections in a multi-layer RNN, it is observed that the capability of the RNN to learn and model high-order dynamics and nonlinearities is significantly improved. The problem of vanishing/exploding gradient in space for a multilayer RNN is also alleviated using feedforward connections. These results are demonstrated using a quadrotor case study, for which a model of the altitude dynamics is learned with our particular network structure, while existing methods are unable to generalize as quickly or at all.
