IEEE Trans Neural Netw Learn Syst. 2014 Apr;25(4):664-76. doi: 10.1109/TNNLS.2013.2280171.
A challenge in modeling type-2 fuzzy logic systems is the development of efficient learning algorithms to cope with the ever increasing size of real-world data sets. In this paper, the extreme learning strategy is introduced to develop a fast training algorithm for interval type-2 Takagi-Sugeno-Kang fuzzy logic systems. The proposed algorithm, called type-2 fuzzy extreme learning algorithm (T2FELA), has two distinctive characteristics. First, the parameters of the antecedents are randomly generated and parameters of the consequents are obtained by a fast learning method according to the extreme learning mechanism. In addition, because the obtained parameters are optimal in the sense of minimizing the norm, the resulting fuzzy systems exhibit better generalization performance. The experimental results clearly demonstrate that the training speed of the proposed T2FELA algorithm is superior to that of the existing state-of-the-art algorithms. The proposed algorithm also shows competitive performance in generalization abilities.
在构建类型 2 模糊逻辑系统的过程中，一个挑战是开发有效的学习算法来应对日益增长的真实数据集的规模。在本文中，引入了极限学习策略来开发用于区间型 2 模糊逻辑系统的快速训练算法。所提出的算法称为类型 2 模糊极限学习算法（T2FELA），它具有两个独特的特点。首先，前件的参数是随机生成的，而后件的参数则根据极限学习机制通过快速学习方法获得。此外，由于获得的参数在范数最小化意义上是最优的，因此得到的模糊系统表现出更好的泛化性能。实验结果清楚地表明，所提出的 T2FELA 算法的训练速度优于现有的最先进算法。该算法在泛化能力方面也表现出竞争性能。
