Determining structural identifiability of parameter learning machines

	Determining structural identifiability of parameter learning machines
	Ran, Zhi-Yong; Hu, Bao-Gang
发表期刊	NEUROCOMPUTING
	2014-03-15
卷号	127 期号:1 页码:88-97
文章类型	Article
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.
关键词	Identifiability Parameter Learning Machine Exhaustive Summary Kullback-leibler Divergence Parameter Redundancy
WOS标题词	Science & Technology ; Technology
关键词[WOS]	GLOBAL IDENTIFIABILITY ; COMPARTMENTAL-MODELS ; PARAMETRIZATIONS ; IDENTIFICATION ; DYNAMICS ; GEOMETRY ; SYSTEMS ; DRIVEN
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:000329603100010
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/2874
专题	多模态人工智能系统全国重点实验室_多媒体计算
作者单位	Chinese Acad Sci, Inst Automat, NLPR&LIAMA, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
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(1):88-97.
APA	Ran, Zhi-Yong,&Hu, Bao-Gang.(2014).Determining structural identifiability of parameter learning machines.NEUROCOMPUTING,127(1),88-97.
MLA	Ran, Zhi-Yong,et al."Determining structural identifiability of parameter learning machines".NEUROCOMPUTING 127.1(2014):88-97.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Ran14.pdf（515KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载