标题： 基于RNN的在线学习：一种具有收敛性保证的一阶优化算法 显示英文标题

标题： RNN-based Online Learning: An Efficient First-Order Optimization Algorithm with a Convergence Guarantee

摘要： 我们研究使用持续运行的循环神经网络（RNN）的在线非线性回归，即基于RNN的在线学习。 对于基于RNN的在线学习，我们引入了一种高效的首阶训练算法，该算法理论上保证能够收敛到最优网络参数。 我们的算法是真正的在线算法，它不需要对学习环境做出任何假设即可保证收敛。 通过数值仿真，我们验证了我们的理论结果，并展示了与最先进的RNN训练方法相比，我们的算法所实现的显著性能提升。 显示英文摘要

摘要： We investigate online nonlinear regression with continually running recurrent neural network networks (RNNs), i.e., RNN-based online learning. For RNN-based online learning, we introduce an efficient first-order training algorithm that theoretically guarantees to converge to the optimum network parameters. Our algorithm is truly online such that it does not make any assumption on the learning environment to guarantee convergence. Through numerical simulations, we verify our theoretical results and illustrate significant performance improvements achieved by our algorithm with respect to the state-of-the-art RNN training methods.