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	Dynamic compensation for an infrared thermometer sensor using least-squares support vector regression (LSSVR) based functional link artificial neural networks (FLANN)
	Dehui Wu ; Songling Huang ; Junjun Xin
	2010-10-12 ; 2010-10-12
关键词	Practical Theoretical or Mathematical/ compensation computerised instrumentation infrared detectors least squares approximations neural nets regression analysis support vector machines temperature measurement thermometers/ infrared thermometer sensor dynamic compensation least squares support vector regression functional link artificial neural networks architecture linear equations inverse model rectification calibration data/ A0762 Detection of radiation (bolometers, photoelectric cells, i.r. and submillimetre waves detection) A0720D Thermometry A0260 Numerical approximation and analysis A0250 Probability theory, stochastic processes, and statistics B7210B Computerised instrumentation B0240Z Other topics in statistics B0290F Interpolation and function approximation (numerical analysis) B7320R Thermal variables measurement B7230C Photodetectors C7410H Computerised instrumentation C3240F Nonelectric transducers and sensing devices C1140Z Other topics in statistics C5290 Neural computing techniques C4130 Interpolation and function approximation (numerical analysis) C6170K Knowledge engineering techniques
中文摘要	A novel functional link artificial neural network (FLANN) architecture is presented and applied to dynamic compensation for an infrared thermometer sensor. The identification results between a generic FLANN and a least-squares support vector regression (LSSVR) are verified to be similar. A new method to update the FLANN weights is derived from LSSVR. Compared with the generic FLANN, the improved one differs markedly in solving a set of linear equations instead of an iterative problem. As a result, more accurate weight evaluations are obtained, and a faster learning course can be expected. The infrared thermometer sensor dynamic compensator is established based on the principle of inverse model rectification, and the improved FLANN is used to describe the compensator. The actual calibration data of the infrared thermometer uIRt/c are used to validate the feasibility of the present method. The experimental results show that the improved FLANN is faster in training speed, higher in precision and more robust.
语种	英语
出版者	IOP Publishing Ltd. ; UK
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/82683]
专题	清华大学
推荐引用方式 GB/T 7714	Dehui Wu,Songling Huang,Junjun Xin. Dynamic compensation for an infrared thermometer sensor using least-squares support vector regression (LSSVR) based functional link artificial neural networks (FLANN)[J],2010, 2010.
APA	Dehui Wu,Songling Huang,&Junjun Xin.(2010).Dynamic compensation for an infrared thermometer sensor using least-squares support vector regression (LSSVR) based functional link artificial neural networks (FLANN)..
MLA	Dehui Wu,et al."Dynamic compensation for an infrared thermometer sensor using least-squares support vector regression (LSSVR) based functional link artificial neural networks (FLANN)".(2010).

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