通过模块化RNN结构提升四旋翼动力学建模能力
Modular Deep Recurrent Neural Network: Application to Quadrotors
- 设计可插拔的模块化RNN，支持灵活架构组合
- 加入前馈跨层连接后，模型学习高阶非线性更强
- 有效缓解深层RNN梯度消失/爆炸问题
提出一种模块化深度循环神经网络（RNN），用于简化不同RNN架构的部署，并自动计算梯度以支持基于梯度的学习。该模块化设计催生了新架构，其中包含前馈跨层连接。在多层RNN中引入此类连接后，其学习和建模高阶动态与非线性的能力显著增强，同时缓解了深层RNN在空间中的梯度消失/爆炸问题。通过四旋翼飞行器案例验证：使用该网络结构成功学习了高度动力学模型，而现有方法无法实现同等程度的泛化或收敛速度。
原文摘要 · Abstract (English)
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.
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