CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Geometric interpretation of nonlinear approximation capability for feedforward neural networks
Hu, BG; Xing, HJ; Yang, YJ; Yin, FL; Wang, J; Guo, CG
Source PublicationADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1
2004
Volume3173Issue:1Pages:7-13
SubtypeArticle
AbstractThis 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.
KeywordNeural Networks
WOS HeadingsScience & Technology ; Technology
Indexed BySCI ; ISTP
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS IDWOS:000223492600002
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7980
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.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
Recommended Citation
GB/T 7714
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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