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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
2008-12-01
发表期刊NEUROCOMPUTING
卷号72期号:1-3页码:392-400
文章类型Article
摘要Identifiability 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.
关键词Identifiability Parameter Redundancy Derivative Functional Vector Nonlinear Regression Hybrid Neural Network
WOS标题词Science & Technology ; Technology
关键词[WOS]PARAMETER REDUNDANCY ; SYMBOLIC COMPUTATION ; DISTINGUISHABILITY
收录类别SCI
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000261643700045
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文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/9617
专题09年以前成果
作者单位1.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
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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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