论文标题
时空激活函数以绘制复杂的动力学系统
Spatio-Temporal Activation Function To Map Complex Dynamical Systems
论文作者
论文摘要
现实世界中的大多数受复杂而混乱的动力学系统的控制。所有这些动态系统都在使用神经网络对它们进行建模方面构成挑战。当前，Reservoir Computing是复发性神经网络的子集，可积极用于模拟复杂的动力系统。在这项工作中，提出了二维激活函数，其中包括一个额外的时间术语，以使其输出赋予动态行为。包括时间术语的包含改变了激活函数的基本性质，它提供了捕获时间序列数据的复杂动力学的能力，而无需依赖复发性神经网络。
Most of the real world is governed by complex and chaotic dynamical systems. All of these dynamical systems pose a challenge in modelling them using neural networks. Currently, reservoir computing, which is a subset of recurrent neural networks, is actively used to simulate complex dynamical systems. In this work, a two dimensional activation function is proposed which includes an additional temporal term to impart dynamic behaviour on its output. The inclusion of a temporal term alters the fundamental nature of an activation function, it provides capability to capture the complex dynamics of time series data without relying on recurrent neural networks.
