论文标题
复发光谱网络（RSN）：塑造离散地图吸引的盆地以达到自动分类
Recurrent Spectral Network (RSN): shaping the basin of attraction of a discrete map to reach automated classification
论文作者
论文摘要
引入了一种新型的自动分类策略，该策略利用了完全训练的动力学系统将属于不同类别的项目转向不同的渐近吸引子。这些后者通过利用操作员的光谱分解来纳入模型，该操作员统治了整个处理网络的线性演化。非线性术语是瞬态的，并允许将作为离散动态系统的初始条件提供的数据解散，从而塑造了不同吸引子的边界。该网络可以配备多个内存内核，可以顺序激活串行数据集处理。我们在这里的新型分类方法（我们在这里术语复发网络（RSN））成功地针对出于说明目的而创建的简单测试模型以及用于图像处理训练的标准数据集。
A novel strategy to automated classification is introduced which exploits a fully trained dynamical system to steer items belonging to different categories toward distinct asymptotic attractors. These latter are incorporated into the model by taking advantage of the spectral decomposition of the operator that rules the linear evolution across the processing network. Non-linear terms act for a transient and allow to disentangle the data supplied as initial condition to the discrete dynamical system, shaping the boundaries of different attractors. The network can be equipped with several memory kernels which can be sequentially activated for serial datasets handling. Our novel approach to classification, that we here term Recurrent Spectral Network (RSN), is successfully challenged against a simple test-bed model, created for illustrative purposes, as well as a standard dataset for image processing training.
