用粒子群优化多简单环储层，提升时间序列预测效率
Structuring Multiple Simple Cycle Reservoirs with Particle Swarm Optimization
- 用多个简单环储层替代单一大储层，通过粒子群优化连接结构
- 在3个基准任务上表现优于现有模型，状态空间更小
- 适合追求高效低功耗的AI硬件应用
储层计算（RC）是一种源自循环神经网络（RNN）的时间高效计算范式。简单环储层（SCR）因其极简设计而突出，具有极低的构建复杂度，并已证明能通用逼近时不变因果衰减记忆滤波器，即使在线性动态情况下亦然。本文提出多简单环储层（MSCR），一种多储层框架，通过用多个相互连接的SCR替换单一大型储层来扩展回声状态网络（ESN）。我们证明，使用粒子群优化（PSO）优化MSCR优于现有多储层模型，在更低维状态空间下实现竞争性预测性能。通过将连接建模为加权有向无环图（DAG），该方法实现了灵活的任务特定拓扑自适应。在三个基准时间序列预测任务上的数值模拟验证了这些优势。研究结果表明，MSCR-PSO是优化多储层系统的有前景框架，为互联SCR的进一步发展和高效AI设备应用奠定了基础。
原文摘要 · Abstract (English)
Reservoir Computing (RC) is a time-efficient computational paradigm derived from Recurrent Neural Networks (RNNs). The Simple Cycle Reservoir (SCR) is an RC model that stands out for its minimalistic design, offering extremely low construction complexity and proven capability of universally approximating time-invariant causal fading memory filters, even in the linear dynamics regime. This paper introduces Multiple Simple Cycle Reservoirs (MSCRs), a multi-reservoir framework that extends Echo State Networks (ESNs) by replacing a single large reservoir with multiple interconnected SCRs. We demonstrate that optimizing MSCR using Particle Swarm Optimization (PSO) outperforms existing multi-reservoir models, achieving competitive predictive performance with a lower-dimensional state space. By modeling interconnections as a weighted Directed Acyclic Graph (DAG), our approach enables flexible, task-specific network topology adaptation. Numerical simulations on three benchmark time-series prediction tasks confirm these advantages over rival algorithms. These findings highlight the potential of MSCR-PSO as a promising framework for optimizing multi-reservoir systems, providing a foundation for further advancements and applications of interconnected SCRs for developing efficient AI devices.
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