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Structural identifiability of generalized constraint neural network models for nonlinear regression
Yang, Shuang-Hong1,2; Hu, Bao-Gang1,2; Cournede, Paul-Henry3
Source PublicationNEUROCOMPUTING
2008-12-01
Volume72Issue:1-3Pages:392-400
SubtypeArticle
AbstractIdentifiability becomes an essential requirement for learning machines when the models contain physically interpretable parameters. This paper presents two approaches to examining structural identifiability of the generalized constraint neural network (GCNN) models by viewing the model from two different perspectives. First, by taking the model as a static deterministic function, a functional framework is established, which can recognize deficient model and at the same time reparameterize it through a pairwise-mode symbolic examination. Second, by viewing the model as the mean function of an isotropic Gaussian conditional distribution, the algebraic approaches [E.A. Catchpole, B.J.T. Morgan, Detecting parameter redundancy, Biometrika 84 (1) (1997) 187-196] are extended to deal with multivariate nonlinear regression models through symbolically checking linear dependence of the derivative functional vectors. Examples are presented in which the proposed approaches are applied to GCNN nonlinear regression models that contain coupling physically interpretable parameters. (C) 2007 Elsevier B.V. All rights reserved.
KeywordIdentifiability Parameter Redundancy Derivative Functional Vector Nonlinear Regression Hybrid Neural Network
WOS HeadingsScience & Technology ; Technology
WOS KeywordPARAMETER REDUNDANCY ; SYMBOLIC COMPUTATION ; DISTINGUISHABILITY
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000261643700045
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9617
Collection09年以前成果
Affiliation1.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100080, Peoples R China
2.Chinese Acad Sci, Inst Automat, LIAMA, Beijing 100080, Peoples R China
3.Ecole Cent Paris, Lab Appl Math & Syst, F-92295 Chatenay Malabry, France
Recommended Citation
GB/T 7714
Yang, Shuang-Hong,Hu, Bao-Gang,Cournede, Paul-Henry. Structural identifiability of generalized constraint neural network models for nonlinear regression[J]. NEUROCOMPUTING,2008,72(1-3):392-400.
APA Yang, Shuang-Hong,Hu, Bao-Gang,&Cournede, Paul-Henry.(2008).Structural identifiability of generalized constraint neural network models for nonlinear regression.NEUROCOMPUTING,72(1-3),392-400.
MLA Yang, Shuang-Hong,et al."Structural identifiability of generalized constraint neural network models for nonlinear regression".NEUROCOMPUTING 72.1-3(2008):392-400.
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