Modular Deep Recurrent Neural Network: Application to Quadrotors

A modular deep Recurrent Neural Network (RNN) is presented that enables easy deployment of various RNN architectures and automatic derivative computation for gradient-based learning. The modular design introduces new architectures, notably one with feedforward inter‑layer connections, which markedly improves the RNN’s ability to learn high‑order dynamics and nonlinearities while mitigating vanishing/exploding gradients. These advantages are illustrated through a quadrotor altitude dynamics case study, where the proposed network learns the model more quickly and generalizes better than existing methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.