Fuzzy broad learning system (FBLS) is a newly proposed fuzzy system, which introduces Takagi–Sugeno fuzzy model into broad learning system. It has shown that FBLS has better nonlinear fitting ability and faster calculation speed than the most of fuzzy neural networks proposed earlier. At the same time, compared to other fuzzy neural networks, FBLS has fewer rules and lower cost of training time. However, label errors or missing are prone to appear in large-scale dataset, which will greatly reduce the performance of FBLS. Therefore, how to use limited label information to train a powerful classifier is an important challenge. In order to address this problem, we introduce Mean-Teacher model for the fuzzy broad learning system. We use the Mean-Teacher model to rebuild the weights of the output layer of FBLS, and use the Teacher–Student model to train FBLS. The proposed model is an implementation of semi-supervised learning which integrates fuzzy logic and broad learning system in the Mean-Teacher-based knowledge distillation framework. Finally, we have proved the great performance of Mean-Teacher-based fuzzy broad learning system (MT-FBLS) through a large number of experiments.

模糊广义学习系统(FBLS)是一种新提出的模糊系统，它将Takagi-Sugeno模糊模型引入到广义学习系统中。结果表明，FBLS比早期提出的大多数模糊神经网络具有更好的非线性拟合能力和更快的计算速度。同时，与其他模糊神经网络相比，FBLS 的规则更少，训练时间成本更低。然而，大规模数据集中容易出现标签错误或缺失，这将大大降低FBLS的性能。因此，如何利用有限的标签信息来训练强大的分类器是一个重要的挑战。为了解决这个问题，我们为模糊广义学习系统引入了Mean-Teacher模型。我们使用Mean-Teacher模型来重建FBLS输出层的权重，并使用Teacher-Student模型来训练FBLS。所提出的模型是半监督学习的实现，它在基于平均教师的知识蒸馏框架中集成了模糊逻辑和广泛的学习系统。最后，我们通过大量实验证明了基于Mean-Teacher的模糊广泛学习系统（MT-FBLS）的优异性能。