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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >Broad learning system: Structural extensions on single-layer and multi-layer neural networks
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Broad learning system: Structural extensions on single-layer and multi-layer neural networks

机译：广泛的学习系统：单层和多层神经网络的结构扩展

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摘要

Broad Learning System proposed recently [1] demonstrates efficient and effective learning capability. Moreover, fast incremental learning algorithms are developed in broad expansions without an entire retraining of the whole model. Compared with the systems in deep structure, the inspired system provides competitive results in classification. In this paper, the broad learning algorithms and incremental learning algorithms are applied to commonly used neural networks, such as radial basis function neural networks (RBF) and hierarchical extremal learning machine (H-ELM). For RBF, the resulting models, called BLS-RBF, are established by regarding the radial basis function as the mapping in the enhancement nodes, and additional enhancement nodes are added if the network needs expansion widely. For H-ELM, the established model, is developed for the incremental extension of multilayer structure. The developed BLS models and algorithms are very effective and efficient in classification. Finally, experimental results are presented.
机译：最近提出的广泛学习系统[1]证明了有效的学习能力。而且，在不对整个模型进行整体重新训练的情况下，快速增量学习算法得到了广泛的发展。与深层结构的系统相比，受启发的系统在分类​​方面提供了竞争优势。本文将广泛的学习算法和增量学习算法应用于常用的神经网络，例如径向基函数神经网络（RBF）和分层极值学习机（H-ELM）。对于RBF，通过将径向基函数视为增强节点中的映射来建立称为BLS-RBF的结果模型，如果网络需要广泛扩展，则添加其他增强节点。对于H-ELM，已建立的模型用于多层结构的增量扩展。所开发的BLS模型和算法在分类中非常有效。最后，给出了实验结果。

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