Determining structural identifiability of parameter learning machines
文献类型：期刊论文
| 作者 | Ran, Zhi-Yong; Hu, Bao-Gang
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2014-03-15 |
| 卷号 | 127页码:88-97 |
| 关键词 | Identifiability Parameter learning machine Exhaustive summary Kullback-Leibler divergence Parameter redundancy |
| 英文摘要 | This paper reports an extension of our previous study on determining structural identifiability of the generalized constraint (GC) models, which are considered to be parameter learning machines. Identifiability defines a uniqueness property to the model parameters. This property is particularly important for those physically interpretable parameters in GC models. We derive identifiability criteria according to the types of models. First, by taking the models as a family of deterministic nonlinear transformations from input space to output space, we provide a criterion for examining identifiability of the Multiple-input Multiple-output (MIMO) models. This result therefore generalizes the previous one for Single-input Single-output (SISO) and Multiple-input Single-output (MISO) models. Second, if considering the models as the mean functions of input-dependent conditional distributions within stochastic framework, we derive an identifiability criterion by means of the Kullback-Leibler divergence (KLD) and regular summary. Third, time-variant models are studied based on the exhaustive summary method. The new identifiability criterion is valid for a range of differential/difference equation models whenever their exhaustive summaries can be obtained. Several model examples from the literature are presented to examine their identifiability property. (C) 2013 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] | GLOBAL IDENTIFIABILITY ; COMPARTMENTAL-MODELS ; PARAMETRIZATIONS ; IDENTIFICATION ; DYNAMICS ; GEOMETRY ; SYSTEMS ; DRIVEN |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000329603100010 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/2874]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 作者单位 | Chinese Acad Sci, Inst Automat, NLPR&LIAMA, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ran, Zhi-Yong,Hu, Bao-Gang. Determining structural identifiability of parameter learning machines[J]. NEUROCOMPUTING,2014,127:88-97. |
| APA | Ran, Zhi-Yong,&Hu, Bao-Gang.(2014).Determining structural identifiability of parameter learning machines.NEUROCOMPUTING,127,88-97. |
| MLA | Ran, Zhi-Yong,et al."Determining structural identifiability of parameter learning machines".NEUROCOMPUTING 127(2014):88-97. |
入库方式： OAI收割
来源：自动化研究所
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