非线性动态系统的二型模糊神经网络辨识--一种新的学习算法及比较研究
Identification of Nonlinear Dynamic Systems Using Type-2 Fuzzy Neural Networks -- A Novel Learning Algorithm and a Comparative Study
摘要：
为了实现更快，更强大的收敛（特别是在嘈杂的工作环境下），滑模理论为基础的学习算法已被提出来调整的前提和结论部分的2型模糊神经网络在本文中。从最近的研究，其中滑模控制理论为基础的规则，提出了只有网络的后件部分，开发的算法适用于完全滑模参数更新规则的前提和后件的2型模糊神经网络。此外，负责分担的贡献的第二型模糊隶属度函数的下部和上部的参数也调整。此外，网络的学习速率在在线训练期间更新。通过适当的李雅普诺夫函数证明了该学习算法的稳定性。实验结果表明，该算法比现有的基于梯度和基于群体智能的方法具有更快的收敛速度。此外，所提出的学习算法具有封闭的形式，它比其他现有的方法更容易实现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
