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Circular Complex-Valued GMDH-Type Neural Network for Real-Valued Classification Problems
Xiao, Jin1; Jia, Yanlin1; Jiang, Xiaoyi2; Wang, Shouyang3
2020-12-01
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
卷号31期号:12页码:5285-5299
摘要Recently, applications of complex-valued neural networks (CVNNs) to real-valued classification problems have attracted significant attention. However, most existing CVNNs are black-box models with poor explanation performance. This study extends the real-valued group method of data handling (RGMDH)-type neural network to the complex field and constructs a circular complex-valued group method of data handling (C-CGMDH)-type neural network, which is a white-box model. First, a complex least squares method is proposed for parameter estimation. Second, a new complex-valued symmetric regularity criterion is constructed with a logarithmic function to represent explicitly the magnitude and phase of the actual and predicted complex output to evaluate and select the middle candidate models. Furthermore, the property of this new complex-valued external criterion is proven to be similar to that of the real external criterion. Before training this model, a circular transformation is used to transform the real-valued input features to the complex field. Twenty-five real-valued classification data sets from the UCI Machine Learning Repository are used to conduct the experiments. The results show that both RGMDH and C-CGMDH models can select the most important features from the complete feature space through a self-organizing modeling process. Compared with RGMDH, the C-CGMDH model converges faster and selects fewer features. Furthermore, its classification performance is statistically significantly better than the benchmark complex-valued and real-valued models. Regarding time complexity, the C-CGMDH model is comparable with other models in dealing with the data sets that have few features. Finally, we demonstrate that the GMDH-type neural network can be interpretable.
关键词Biological system modeling Data models Biological neural networks Predictive models Neurons Mathematical model Complex-valued external criterion complex-valued group method of data handling (GMDH)-type neural network parameter estimation real-valued classification
DOI10.1109/TNNLS.2020.2966031
收录类别SCI
语种英语
资助项目Major Project of the National Social Science Foundation of China[18VZL006] ; EU Horizon 2020 RISE Project ULTRACEPT[778062] ; National Natural Science Foundation of China[71471124] ; Tianfu TenThousand Talents Program of Sichuan Province ; Excellent Youth Fund of Sichuan University[skqx201607] ; Excellent Youth Fund of Sichuan University[sksyl201709] ; Excellent Youth Fund of Sichuan University[skzx2016-rcrw14] ; Leading Cultivation Talents Program of Sichuan University ; Teacher and Student Joint Innovation Project of Business School of Sichuan University[LH2018011]
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000595533300020
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型期刊论文
条目标识符http://ir.amss.ac.cn/handle/2S8OKBNM/57841
专题中国科学院数学与系统科学研究院
通讯作者Wang, Shouyang
作者单位1.Sichuan Univ, Business Sch, Chengdu 610064, Peoples R China
2.Univ Munster, Fac Math & Comp Sci, D-48149 Munster, Germany
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714
Xiao, Jin,Jia, Yanlin,Jiang, Xiaoyi,et al. Circular Complex-Valued GMDH-Type Neural Network for Real-Valued Classification Problems[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,31(12):5285-5299.
APA Xiao, Jin,Jia, Yanlin,Jiang, Xiaoyi,&Wang, Shouyang.(2020).Circular Complex-Valued GMDH-Type Neural Network for Real-Valued Classification Problems.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,31(12),5285-5299.
MLA Xiao, Jin,et al."Circular Complex-Valued GMDH-Type Neural Network for Real-Valued Classification Problems".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 31.12(2020):5285-5299.
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