自组织循环随机配置网络提升非平稳数据持续学习能力
Self-Organizing Recurrent Stochastic Configuration Networks for Nonstationary Data Modelling
- 动态调整网络结构与参数，实时响应数据流变化
- 在多个非平稳数据集上优于ESN、RSCN等模型，泛化性能强
- 适合需要长期在线学习的工业系统建模场景
循环随机配置网络（RSCN）是一类有潜力的随机学习模型，适用于非线性动态建模。然而，在工业系统中，数据常具有非平稳特性，导致模型在训练数据上表现良好，却难以适应新到来的数据。本文提出一种自组织版本的RSCN（SORSCN），以增强网络对非平稳数据的持续学习能力。SORSCN能根据实时数据流自主调整网络参数和储备池结构。输出权重通过投影算法在线更新，网络结构则基于递归随机配置算法和改进的敏感性分析动态调整。在回声状态网络（ESN）、在线自学习随机配置网络（OSL-SCN）、自组织模块化回声状态网络（SOMESN）、RSCN与SORSCN之间的综合对比实验表明，所提出的SORSCN在泛化性能上显著优于其他模型，展现出在非平稳非线性系统建模中的巨大潜力。
原文摘要 · Abstract (English)
Recurrent stochastic configuration networks (RSCNs) are a class of randomized learner models that have shown promise in modelling nonlinear dynamics. In many fields, however, the data generated by industry systems often exhibits nonstationary characteristics, leading to the built model performing well on the training data but struggling with the newly arriving data. This paper aims at developing a self-organizing version of RSCNs, termed as SORSCNs, to enhance the continuous learning ability of the network for modelling nonstationary data. SORSCNs can autonomously adjust the network parameters and reservoir structure according to the data streams acquired in real-time. The output weights are updated online using the projection algorithm, while the network structure is dynamically adjusted in the light of the recurrent stochastic configuration algorithm and an improved sensitivity analysis. Comprehensive comparisons among the echo state network (ESN), online self-learning stochastic configuration network (OSL-SCN), self-organizing modular ESN (SOMESN), RSCN, and SORSCN are carried out. Experimental results clearly demonstrate that the proposed SORSCNs outperform other models with sound generalization, indicating great potential in modelling nonlinear systems with nonstationary dynamics.
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