Geometric interpretation of nonlinear approximation capability for feedforward neural networks
Hu, BG; Xing, HJ; Yang, YJ; Yin, FL; Wang, J; Guo, CG
2004
发表期刊ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1
卷号3173期号:1页码:7-13
文章类型Article
摘要This paper presents a preliminary study on the nonlinear approximation capability of feedforward neural networks (FNNs) via a geometric approach. Three simplest FNNs with at most four free parameters are defined and investigated. By approximations on one-dimensional functions, we observe that the Chebyshev-polynomials, Gaussian, and sigmoidal FNNs are ranked in order of providing more varieties of non-linearities. If neglecting the compactness feature inherited by Gaussian neural networks, we consider that the Chebyshev-polynomial-based neural networks will be the best among three types of FNNs in an efficient use of free parameters.
关键词Neural Networks
WOS标题词Science & Technology ; Technology
收录类别SCI ; ISTP
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号WOS:000223492600002
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/7980
专题模式识别国家重点实验室_多媒体计算与图形学
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
2.Chinese Acad Sci, Beijing Grad Sch, Beijing 100080, Peoples R China
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Hu, BG,Xing, HJ,Yang, YJ,et al. Geometric interpretation of nonlinear approximation capability for feedforward neural networks[J]. ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1,2004,3173(1):7-13.
APA Hu, BG,Xing, HJ,Yang, YJ,Yin, FL,Wang, J,&Guo, CG.(2004).Geometric interpretation of nonlinear approximation capability for feedforward neural networks.ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1,3173(1),7-13.
MLA Hu, BG,et al."Geometric interpretation of nonlinear approximation capability for feedforward neural networks".ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1 3173.1(2004):7-13.
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