General Type-2 Radial Basis Function Neural Network: A Data-Driven Fuzzy Model
General Type-2 Radial Basis Function Neural Network: A Data-Driven Fuzzy Model
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DOI：
10.1109/tfuzz.2018.2858740
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发表时间：
2019-02-01
影响因子：
11.9
通讯作者：
Panoutsos, George
中科院分区：
文献类型：
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作者：
Rubio-Solis, Adrian;Melin, Patricia;Panoutsos, George
本文提出了一种新的通用型-2径向基函数神经网络（GT2- rbfnn），它在功能上等价于Takagi-Sugeno- Kang （TSK）或Mamdani型的GT2模糊逻辑系统（FLS）。GT2- rbfnn的神经结构基于α -平面表示，其中每个模糊规则的前、后部分使用GT2模糊集（FSs）。为了减少Karnik-Mendel算法的迭代性，使用了增强的Karnik-Mendel （EKM）类型约简和三种流行的直接去模糊化方法，即1)Nie-Tan方法（NT）， 2) WU - mendel不确定界方法（WU）和3)Biglarbegian-Melek-Mendel算法（BMM）。因此，本文提供了四种不同的GT2-RBFNN架构及其参数优化。这种优化是一种两阶段的方法，首先实现迭代信息粒化（IIG）方法来估计每个模糊规则的前置参数。其次，采用自适应梯度下降法（AGD）分别对GT2-RBFNN的每个结果部分和模糊规则库进行优化。考虑了一些流行的基准数据集，非线性系统的识别和混沌时间序列的预测。报告的实验结果的比较分析用于评估建议的GT2 RBFNN相对于其他流行的方法的性能。
This paper proposes a new General Type-2 Radial Basis Function Neural Network (GT2-RBFNN) that is functionally equivalent to a GT2 Fuzzy Logic System (FLS) of either Takagi-Sugeno- Kang (TSK) or Mamdani type. The neural structure of the GT2-RBFNN is based on the alpha-planes representation, in which the antecedent and consequent part of each fuzzy rule uses GT2 Fuzzy Sets (FSs). To reduce the iterative nature of the Karnik-Mendel algorithm, the Enhaned-Karnik-Mendel (EKM) type-reduction and three popular direct-defuzzification methods, namely the 1) Nie-Tan approach (NT), the 2) Wu-Mendel uncertain bounds method (WU) and the 3) Biglarbegian-Melek-Mendel algorithm (BMM) are used. Hence, this paper provides four different architectures of the GT2-RBFNN and their parametric optimisation. Such optimisation is a two-stage methodology that first implements an Iterative Information Granulation (IIG) approach to estimate the antecedent parameters of each fuzzy rule. Secondly, each consequent part and the fuzzy rule base of the GT2-RBFNN is optimised using an Adaptive Gradient Descent method (AGD) respectively. A number of popular benchmark data sets, the identification of a nonlinear system and the prediction of chaotic time series are considered. The reported comparative analysis of experimental results is used to evaluate the performance of the suggested GT2 RBFNN with respect to other popular methodologies.