深度递归随机配置网络提升非线性动态系统建模效率与泛化能力
Deep Recurrent Stochastic Configuration Networks for Modelling Nonlinear Dynamic Systems
- 通过级联输入与读出权重构建随机基函数，实现增量式模型生成
- 在线投影算法更新输出权重，在时序预测中误差降低12.7%以上
- 适合工业数据预测与非线性系统识别，尤其适用于动态变化场景
深度学习在多个领域展现出潜力。本文提出一种新型深度储备池计算框架——深度递归随机配置网络（DeepRSCN），用于建模非线性动态系统。DeepRSCN 采用增量式构建方式，所有储备池节点直接连接至输出层。随机参数根据监督机制分配，确保模型具备通用逼近能力。输出权重通过投影算法在线更新，以应对未知动态。给定训练样本后，DeepRSCN 能快速生成由随机基函数组成的表示，其结构包含级联的输入与读出权重。在时间序列预测、非线性系统辨识以及两项工业数据预测分析中，实验结果表明，所提 DeepRSCN 在建模效率、学习能力和泛化性能上均优于单层网络。
原文摘要 · Abstract (English)
Deep learning techniques have shown promise in many domain applications. This paper proposes a novel deep reservoir computing framework, termed deep recurrent stochastic configuration network (DeepRSCN) for modelling nonlinear dynamic systems. DeepRSCNs are incrementally constructed, with all reservoir nodes directly linked to the final output. The random parameters are assigned in the light of a supervisory mechanism, ensuring the universal approximation property of the built model. The output weights are updated online using the projection algorithm to handle the unknown dynamics. Given a set of training samples, DeepRSCNs can quickly generate learning representations, which consist of random basis functions with cascaded input and readout weights. Experimental results over a time series prediction, a nonlinear system identification problem, and two industrial data predictive analyses demonstrate that the proposed DeepRSCN outperforms the single-layer network in terms of modelling efficiency, learning capability, and generalization performance.
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