Generalized Constraint Neural Network Regression Model Subject to Linear Priors

	Generalized Constraint Neural Network Regression Model Subject to Linear Priors
	Qu, Ya-Jun; Hu, Bao-Gang
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS
	2011-12-01
卷号	22 期号:12 页码:2447-2459
文章类型	Article
摘要	This paper is reports an extension of our previous investigations on adding transparency to neural networks. We focus on a class of linear priors (LPs), such as symmetry, ranking list, boundary, monotonicity, etc., which represent either linear-equality or linear-inequality priors. A generalized constraint neural network-LPs (GCNN-LPs) model is studied. Unlike other existing modeling approaches, the GCNN-LP model exhibits its advantages. First, any LP is embedded by an explicitly structural mode, which may add a higher degree of transparency than using a pure algorithm mode. Second, a direct elimination and least squares approach is adopted to study the model, which produces better performances in both accuracy and computational cost over the Lagrange multiplier techniques in experiments. Specific attention is paid to both "hard (strictly satisfied)" and "soft (weakly satisfied)" constraints for regression problems. Numerical investigations are made on synthetic examples as well as on the real-world datasets. Simulation results demonstrate the effectiveness of the proposed modeling approach in comparison with other existing approaches.
关键词	Linear Constraints Linear Priors Nonlinear Regression Radial Basis Function Networks Transparency
WOS标题词	Science & Technology ; Technology
关键词[WOS]	SUPPORT VECTOR MACHINES ; INCORPORATING PRIOR KNOWLEDGE ; KERNEL APPROXIMATION ; EXTRACTION
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000299082900027
引用统计	被引频次：29[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/173211/2805
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, Natl Lab Pattern Recognit, Inst Automat, 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	Qu, Ya-Jun,Hu, Bao-Gang. Generalized Constraint Neural Network Regression Model Subject to Linear Priors[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS,2011,22(12):2447-2459.
APA	Qu, Ya-Jun,&Hu, Bao-Gang.(2011).Generalized Constraint Neural Network Regression Model Subject to Linear Priors.IEEE TRANSACTIONS ON NEURAL NETWORKS,22(12),2447-2459.
MLA	Qu, Ya-Jun,et al."Generalized Constraint Neural Network Regression Model Subject to Linear Priors".IEEE TRANSACTIONS ON NEURAL NETWORKS 22.12(2011):2447-2459.