Abstract
We introduce a novel class of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) paradigm, called Residual Reservoir Memory Networks (ResRMNs). ResRMN combines a linear memory reservoir with a non-linear reservoir, where the latter is based on residual orthogonal connections along the temporal dimension for enhanced long-term propagation of the input. The resulting reservoir state dynamics are studied through the lens of linear stability analysis, and we investigate diverse configurations for the temporal residual connections. The proposed approach is empirically assessed on time-series and pixel-level 1-D classification tasks. Our experimental results highlight the advantages of the proposed approach over other conventional RC models.
Abstract (translated)
我们介绍了一种新型未经训练的循环神经网络（RNN）类别，该类别在水库计算（RC）框架下被命名为残差水库记忆网络（ResRMN）。ResRMN结合了线性内存水库和非线性水库，其中后者基于时间维度上的残差正交连接来增强输入信号的长期传播。我们通过线性稳定性分析研究了由此产生的水库状态动力学，并探讨了时间残差连接的各种配置。我们的方法在时间序列和像素级1-D分类任务上进行了经验评估。实验结果突显了所提出的方法相对于其他传统RC模型的优势。
URL
https://arxiv.org/abs/2508.09925
