Xia Ji, Chu Junyu, Leng Siyang, Ma Huanfei
School of Mathematical Sciences, Soochow University, Suzhou 215001, China.
Academy for Engineering and Technology and Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China.
Chaos. 2023 Nov 1;33(11). doi: 10.1063/5.0156224.
Reservoir computing (RC), a variant recurrent neural network, has very compact architecture and ability to efficiently reconstruct nonlinear dynamics by combining both memory capacity and nonlinear transformations. However, in the standard RC framework, there is a trade-off between memory capacity and nonlinear mapping, which limits its ability to handle complex tasks with long-term dependencies. To overcome this limitation, this paper proposes a new RC framework called neural delayed reservoir computing (ND-RC) with a chain structure reservoir that can decouple the memory capacity and nonlinearity, allowing for independent tuning of them, respectively. The proposed ND-RC model offers a promising solution to the memory-nonlinearity trade-off problem in RC and provides a more flexible and effective approach for modeling complex nonlinear systems with long-term dependencies. The proposed ND-RC framework is validated with typical benchmark nonlinear systems and is particularly successful in reconstructing and predicting the Mackey-Glass system with high time delays. The memory-nonlinearity decoupling ability is further confirmed by several standard tests.
储层计算（RC）是一种变体递归神经网络，具有非常紧凑的架构，并且能够通过结合记忆容量和非线性变换来有效地重构非线性动力学。然而，在标准的RC框架中，记忆容量和非线性映射之间存在权衡，这限制了其处理具有长期依赖性的复杂任务的能力。为了克服这一限制，本文提出了一种新的RC框架，称为神经延迟储层计算（ND-RC），它具有链式结构储层，能够将记忆容量和非线性解耦，从而分别对它们进行独立调整。所提出的ND-RC模型为RC中的记忆-非线性权衡问题提供了一个有前景的解决方案，并为建模具有长期依赖性的复杂非线性系统提供了一种更灵活有效的方法。所提出的ND-RC框架通过典型的基准非线性系统进行了验证，并且在重构和预测具有高时间延迟的Mackey-Glass系统方面特别成功。通过几个标准测试进一步证实了记忆-非线性解耦能力。
