The broad learning system (BLS) has been identified as an important research topic in machine learning. However, the typical BLS suffers from poor robustness for uncertainties because of its characteristic of the deterministic representation. To overcome this problem, a type-2 fuzzy BLS (FBLS) is designed and analyzed in this article. First, a group of interval type-2 fuzzy neurons was used to replace the feature neurons of BLS. Then, the representation of BLS can be improved to obtain good robustness. Second, a fuzzy pseudoinverse learning algorithm was designed to adjust the parameter of type-2 FBLS. Then, the proposed type-2 FBLS was able to maintain the fast computational nature of BLS. Third, a theoretical analysis on the convergence of type-2 FBLS was given to show the computational efficiency. Finally, some benchmark and practical problems were used to test the merits of type-2 FBLS. The experimental results indicated that the proposed type-2 FBLS can achieve outstanding performance.

中文翻译：

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2型模糊广义学习系统


广泛学习系统（BLS）已被确定为机器学习的重要研究课题。然而，典型的BLS由于其确定性表示的特点，对不确定性的鲁棒性较差。为了克服这个问题，本文设计并分析了2型模糊BLS（FBLS）。首先，用一组区间2型模糊神经元代替BLS的特征神经元。然后，可以改进BLS的表示以获得良好的鲁棒性。其次，设计了模糊伪逆学习算法来调整2型FBLS的参数。然后，所提出的 2 型 FBLS 能够保持 BLS 的快速计算特性。第三，对2型FBLS的收敛性进行了理论分析，以显示其计算效率。最后，使用一些基准和实际问题来测试2型FBLS的优点。实验结果表明，所提出的2型FBLS可以实现出色的性能。