通过增量块结构提升随机配置网络对复杂动态系统的建模效率。
Recurrent Stochastic Configuration Networks with Incremental Blocks
- 采用增量块机制并行添加多个子储藏层，结构由监督机制自适应确定。
- 在时序预测与工业数据任务中，建模效率与泛化性能优于传统方法。
- 适合需要高效在线学习的复杂动态系统建模场景。
循环随机配置网络（RSCNs）在处理具有顺序不确定性的非线性动态系统方面表现出色，具备易实现、低人工干预和强逼近能力。本文提出一种基于块增量的改进结构——块式RSCN（BRSCN），可同时添加多个储藏节点（子储藏层），每个子储藏层根据监督机制配置独特结构，确保其具有普遍逼近性。通过适当缩放储藏反馈矩阵，保障网络的回声状态特性。输出权重采用投影算法在线更新，并建立了促进参数收敛的持续激励条件。在时间序列预测、非线性系统辨识及两项工业数据预测分析任务中，实验结果表明，所提BRSCN在建模效率、学习能力和泛化性能上均表现优异，展现出应对复杂动态系统的巨大潜力。
原文摘要 · Abstract (English)
Recurrent stochastic configuration networks (RSCNs) have shown promise in modelling nonlinear dynamic systems with order uncertainty due to their advantages of easy implementation, less human intervention, and strong approximation capability. This paper develops the original RSCNs with block increments, termed block RSCNs (BRSCNs), to further enhance the learning capacity and efficiency of the network. BRSCNs can simultaneously add multiple reservoir nodes (subreservoirs) during the construction. Each subreservoir is configured with a unique structure in the light of a supervisory mechanism, ensuring the universal approximation property. The reservoir feedback matrix is appropriately scaled to guarantee the echo state property of the network. Furthermore, the output weights are updated online using a projection algorithm, and the persistent excitation conditions that facilitate parameter convergence are also established. Numerical results over a time series prediction, a nonlinear system identification task, and two industrial data predictive analyses demonstrate that the proposed BRSCN performs favourably in terms of modelling efficiency, learning, and generalization performance, highlighting their significant potential for coping with complex dynamics.
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