The broad learning system (BLS) is an emerging approach for effective and efficient modeling of complex systems. The inputs are transferred and placed in the feature nodes, and then sent into the enhancement nodes for nonlinear transformation. The structure of a BLS can be extended in a wide sense. Incremental learning algorithms are designed for fast learning in broad expansion. Based on the typical BLSs, a novel recurrent BLS (RBLS) is proposed in this paper. The nodes in the enhancement units of the BLS are recurrently connected, for the purpose of capturing the dynamic characteristics of a time series. A sparse autoencoder is used to extract the features from the input instead of the randomly initialized weights. In this way, the RBLS retains the merit of fast computing and fits for processing sequential data. Motivated by the idea of “fine-tuning” in deep learning, the weights in the RBLS can be updated by conjugate gradient methods if the prediction errors are large. We exhibit the merits of our proposed model on several chaotic time series. Experimental results substantiate the effectiveness of the RBLS. For chaotic benchmark datasets, the RBLS achieves very small errors, and for the real-world dataset, the performance is satisfactory.

中文翻译：

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递归广义学习系统，用于时间序列预测

广泛的学习系统（BLS）是对复杂系统进行有效建模的一种新兴方法。输入被传输并放置在特征节点中，然后发送到增强节点以进行非线性变换。BLS的结构可以在广义上扩展。增量学习算法设计用于广泛扩展的快速学习。基于典型的BLS，本文提出了一种新型的递归BLS（RBLS）。为了捕获时间序列的动态特性，将BLS增强单元中的节点循环连接。稀疏自动编码器用于从输入中提取特征，而不是从随机初始化的权重中提取特征。这样，RBLS保留了快速计算的优点，适合处理顺序数据。出于深度学习中“微调”的思想的推动，如果预测误差较大，则可以通过共轭梯度法来更新RBLS中的权重。我们在几个混沌时间序列上展示了我们提出的模型的优点。实验结果证实了RBLS的有效性。对于混沌基准数据集，RBLS的误差很小，而对于真实世界的数据集，其性能令人满意。