In order to achieve faster and more robust convergence (especially under noisy working environments), a sliding mode theory-based learning algorithm has been proposed to tune both the premise and consequent parts of type-2 fuzzy neural networks in this paper. Differently from recent studies, where sliding mode control theory-based rules are proposed for only the consequent part of the network, the developed algorithm applies fully sliding mode parameter update rules for both the premise and consequent parts of the type-2 fuzzy neural networks. In addition, the responsible parameter for sharing the contributions of the lower and upper parts of the type-2 fuzzy membership functions is also tuned. Moreover, the learning rate of the network is updated during the online training. The stability of the proposed learning algorithm has been proved by using an appropriate Lyapunov function. Several comparisons have been realized and shown that the proposed algorithm has faster convergence speed than the existing methods such as gradient-based and swarm intelligence-based methods. Moreover, the proposed learning algorithm has a closed form, and it is easier to implement than the other existing methods.

翻译：为了实现更快、更强有力的趋同(特别是在吵闹的工作环境中),已提议采用滑动模式理论理论学习算法,以调和本文中第2类模糊神经网络的前提和随后部分。与最近研究不同,即只对网络随后的部分提出滑动模式控制理论规则,发达算法对第2类模糊神经网络的前提和随后部分适用完全滑动模式参数更新规则。此外,还调整了分担第2类模糊成员功能下层和上层部分贡献的责任参数。此外,网络的学习率在网上培训期间得到更新。使用适当的Lyapunov功能证明了拟议的学习算法的稳定性。一些比较已经实现,并表明拟议的算法比现有方法(例如梯度法和温热情报方法)更快的趋同速度。此外,拟议的学习算法有封闭的形式,比其他现有方法更容易实施。