Complex-valued GMDH-type neural network for real-valued classification problems

被引：2

作者：

Xiao, Jin ^{[1
]}

Hu, Yi ^{[2
]}

Wang, Shouyang ^{[3
]}

机构：

[1] Sichuan Univ, Sch Business, Chengdu, Peoples R China

[2] Univ Chinese Acad Sci, Sch Management, Beijing 100080, Peoples R China

[3] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China

来源：

2013 SIXTH INTERNATIONAL CONFERENCE ON BUSINESS INTELLIGENCE AND FINANCIAL ENGINEERING (BIFE) | 2014年

基金：

中国博士后科学基金;

关键词：

real-valued classification; complex-valued neural networks; real-valued GMDH; complex-valued GMDH; ENSEMBLE;

D O I：

10.1109/BIFE.2013.16

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Recently, the application of complex-valued neural networks (CVNNs) for real-valued classification has attracted more and more attention. To overcome the limitations of the existing CVNNs, this study extends the real-valued group method of data handling (RGMDH) type neural network to complex domain, and constructs complex-valued GMDH-type neural network (CGMDH). First, it proposes the complex least squares for parameter estimation, and then constructs the complex external criterion to evaluate and select the middle candidate models. We conduct experiments in 10 UCI real-valued classification datasets. The results show that the performance of CGMDH is better than that of RGMDH and other four models. At the same time, the convergence speed of CGMDH is faster than that of RGMDH.

引用

页码：70 / 74

页数：5

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