Hybrid Fuzzy Wavelet Neural Networks Architecture Based on Polynomial Neural Networks and Fuzzy Set/Relation Inference-Based Wavelet Neurons
Hybrid Fuzzy Wavelet Neural Networks Architecture Based on Polynomial Neural Networks and Fuzzy Set/Relation Inference-Based Wavelet Neurons
复制标题
基于多项式神经网络和基于模糊集/关系推理的小波神经元的混合模糊小波神经网络体系结构
DOI：
10.1109/tnnls.2017.2729589
复制
发表时间：
2018-08-01
影响因子：
10.4
通讯作者：
Pedrycz, Witold
中科院分区：
文献类型：
--
作者：
Huang, Wei;Oh, Sung-Kwun;Pedrycz, Witold
本文提出了一种借助多项式神经网络（PNN）和基于模糊推理的小波神经元（FIWN）实现的混合模糊小波神经网络（HFWNN）。提出了基于模糊集推理的小波神经元和基于模糊关系推理的小波神经元两种模糊小波网络。特别是，没有任何模糊集组件的FIWN（即，模糊规则的前提部分）变成小波神经元（WN）。为了克服传统小波神经网络或模糊小波神经网络参数完全随机确定的局限性，采用C-均值聚类方法对FIWNs或WN中的小波函数参数进行初始化. HFWNN的整体架构类似于典型的PNN之一。HFWNN的主要设计策略如下。首先，网络的第一层由FIWN（例如，FSIWN或FRIWN），用于反映数据的不确定性，而第二层和更高层由WN组成，其表现出高度的灵活性并实现小波函数的线性组合。其次，HFWNN的设计中使用的参数进行调整，通过遗传优化。为了评估所提出的HFWNN的性能，考虑了几个公开的数据。此外，还进行了全面的比较分析。
This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.