Structural identifiability of generalized constraint neural network models for nonlinear regression
文献类型：期刊论文
| 作者 | Yang, Shuang-Hong1,2; Hu, Bao-Gang1,2 ; Cournede, Paul-Henry3
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2008-12-01 |
| 卷号 | 72期号:1-3页码:392-400 |
| 关键词 | Identifiability Parameter redundancy Derivative functional vector Nonlinear regression Hybrid neural network |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | PARAMETER REDUNDANCY ; SYMBOLIC COMPUTATION ; DISTINGUISHABILITY |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000261643700045 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9617]  |
| 专题 | 自动化研究所_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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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