Variational learning for Generalized Associative Functional Networks in modeling dynamic process of plant growth

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 多媒体计算与图形学团队

	Variational learning for Generalized Associative Functional Networks in modeling dynamic process of plant growth
	Qu, Han-Bing 1; Hu, Bao-Gang2,3
刊名	ECOLOGICAL INFORMATICS
	2009-08-01
卷号	4 期号:3 页码:163-176
关键词	Functional equations Generalized Associative Functional Networks Variational Bayes Bayesian backfitting Dynamic plant growth modeling
英文摘要	This paper presents a new statistical techniques - Bayesian Generalized Associative Functional Networks (GAFN), to model the dynamical plant growth process of greenhouse crops. GAFNs are able to incorporate the domain knowledge and data to model complex ecosystem. By use of the functional networks and Bayesian framework, the prior knowledge can be naturally embedded into the model, and the functional relationship between inputs and outputs can be learned during the training process. Our main interest is focused on the Generalized Associative Functional Networks (GAFNs), which are appropriate to model multiple variable processes. Three main advantages are obtained through the applications of Bayesian GAFN methods to modeling dynamic process of plant growth. Firstly, this approach provides a powerful tool for revealing some useful relationships between the greenhouse environmental factors and the plant growth parameters. Secondly. Bayesian GAFN can model Multiple-Input Multiple-Output (MIMO) systems from the given data, and presents a good generalization capability from the final single model for successfully fitting ail 12 data sets over 5-year field experiments. Thirdly, the Bayesian GAFN method can also play as an optimization tool to estimate the interested parameter in the agro-ecosystem. In this work, two algorithms are proposed for the statistical inference of parameters in GAFNs. Both of them are based on the variational inference, also called variational Bayes (VB) techniques, which may provide probabilistic interpretations for the built models. VB-based learning methods are able to yield estimations of the full posterior probability of model parameters. Synthetic and real-world examples are implemented to confirm the validity of the proposed methods. (C) 2009 Elsevier B.V. All rights reserved.
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
类目[WOS]	Ecology
研究领域[WOS]	Environmental Sciences & Ecology
关键词[WOS]	ARTIFICIAL NEURAL-NETWORKS ; MIXTURE-MODELS ; INFORMATION ; REGRESSION ; BAYES ; VALIDATION ; PREDICTION ; KNOWLEDGE ; TOOL
收录类别	SCI
语种	英语
WOS记录号	WOS:000269597300006
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/2818]
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	1.Beijing Acad Sci & Technol, Beijing Res Ctr Pattern Recognit Technol, Beijing 100012, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100080, Peoples R China 3.Chinese Acad Sci, Beijing Grad Sch, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Qu, Han-Bing,Hu, Bao-Gang. Variational learning for Generalized Associative Functional Networks in modeling dynamic process of plant growth[J]. ECOLOGICAL INFORMATICS,2009,4(3):163-176.
APA	Qu, Han-Bing,&Hu, Bao-Gang.(2009).Variational learning for Generalized Associative Functional Networks in modeling dynamic process of plant growth.ECOLOGICAL INFORMATICS,4(3),163-176.
MLA	Qu, Han-Bing,et al."Variational learning for Generalized Associative Functional Networks in modeling dynamic process of plant growth".ECOLOGICAL INFORMATICS 4.3(2009):163-176.