Term Selection for a Class of Separable Nonlinear Models - Details

author：

Gan, Min (Gan, Min.) ^[1] | Chen, Guang-Yong (Chen, Guang-Yong.) ^[2] | Chen, Long (Chen, Long.) ^[3] | Chen, C. L. Philip (Chen, C. L. Philip.) ^[4]

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Abstract：

In　this　paper,　we　consider　the　term　selection　problem　for　a　class　of　separable　nonlinear　models.　The　strategy　is　a　two-step　process　in　which　the　nonlinear　parameters　of　the　model　are　first　optimized　by　a　variable　projection　method,　and　then　the　least　absolute　shrinkage　and　selection　operator　are　adopted　to　obtain　a　sparse　solution　by　picking　out　the　critical　terms　automatically.　This　process　may　be　repeated　several　times.　The　proposed　algorithm　is　tested　on　parameter　estimation　problems　for　an　exponential　model　and　a　neural　network-based　model.　The　numerical　results　show　that　the　proposed　algorithm　can　pick　out　the　appropriate　terms　from　the　overparameterized　model　and　the　obtained　parsimonious　model　performs　better　than　other　methods.　©　2012　IEEE.

Keyword：

Nonlinear systems Numerical methods Regression analysis Shrinkage

Community：

[ 1 ] [Gan, Min]College of Mathematics and Computer Science, Fuzhou University, Fuzhou; 350116, China
[ 2 ] [Gan, Min]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350116, China
[ 3 ] [Gan, Min]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou; 350003, China
[ 4 ] [Gan, Min]Center for Discrete Mathematics and Theoretical Computer Science, Fuzhou University, Fuzhou; 350116, China
[ 5 ] [Chen, Guang-Yong]College of Mathematics and Computer Science, Fuzhou University, Fuzhou; 350116, China
[ 6 ] [Chen, Guang-Yong]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350116, China
[ 7 ] [Chen, Guang-Yong]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou; 350003, China
[ 8 ] [Chen, Guang-Yong]Center for Discrete Mathematics and Theoretical Computer Science, Fuzhou University, Fuzhou; 350116, China
[ 9 ] [Chen, Long]Faculty of Science and Technology, University of Macau, 99999, China
[ 10 ] [Chen, C. L. Philip]Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, 99999, China
[ 11 ] [Chen, C. L. Philip]Navigation College, Dalian Maritime University, Dalian; 116026, China
[ 12 ] [Chen, C. L. Philip]Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an; 710072, China

Reprint 's Address：

[chen, guang-yong]key laboratory of spatial data mining and information sharing, ministry of education, fuzhou; 350003, china;;[chen, guang-yong]college of mathematics and computer science, fuzhou university, fuzhou; 350116, china;;[chen, guang-yong]center for discrete mathematics and theoretical computer science, fuzhou university, fuzhou; 350116, china;;[chen, guang-yong]fujian provincial key laboratory of network computing and intelligent information processing, fuzhou university, fuzhou; 350116, china

Email：

cgykeda@mail.ustc.edu.cn

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Source ：

IEEE Transactions on Neural Networks and Learning Systems

ISSN： 2162-237X

Year： 2020

Issue： 2

Volume： 31

Page： 445-451

1 0 . 4 5 1

JCR@2020

1 0 . 2 0 0

JCR@2023

ESI HC Threshold：149

JCR Journal Grade：1

CAS Journal Grade：1

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count： 72

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 1

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