Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menof 32852, Egypt.
ISA Trans. 2018 Jan;72:205-217. doi: 10.1016/j.isatra.2017.10.012. Epub 2017 Oct 31.
In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs.
在这项研究中，引入了一种新型的递归区间型 2 阶 Takagi-Sugeno-Kang（TSK）模糊神经网络（FNN）结构，用于非线性动态和时变系统的辨识。它结合了二阶模糊集（T2FSs）和递归 FNN，以避免数据不确定性。所提出结构中的模糊触发强度被作为内部变量返回到网络输入。区间型 2 模糊集（IT2FSs）用于描述每个规则的前件，而后件是 TSK 型，它是内部变量和外部输入的线性函数，具有区间权重。所提出的 RIT2TSKFNN 的所有类型 2 模糊规则都是基于结构和参数学习在在线学习的，这是使用类型 2 模糊聚类完成的。根据 Lyapunov 函数对所提出的 RIT2TSKFNN 的前件和后件参数进行更新，以实现网络稳定性。所得结果表明，与其他类型 2 FNN 相比，我们提出的网络具有较小的均方根误差（RMSE）和较小的平方误差积分（ISE），并且具有较少的规则和较小的计算时间。
