NDLI: Parsimonious modeling for sleep fidget soft sensor based on kernel methods

Content Provider	IEEE Xplore Digital Library
Author	Taifu Li Yingying Su JinLiang Shi Debiao Wang
Copyright Year	2008
Description	Author affiliation: Coll. of Electron. Inf. Eng., Chongqing Univ. of Sci. & Technol., Chongqing (Taifu Li; Yingying Su; JinLiang Shi; Debiao Wang)
Abstract	It is very normal that the soft sensor model based on computational intelligence would adopt so many secondary variables, so there is redundant correlative information in input space, it is easy to appear the curse of dimensionality, and the complexity of model is large. In order to obtain parsimonious sleep fidget soft sensor model that is high at precision and low at complexity, authors adopt the feature extraction methods such as principal component analysis (PCA) and kernel principal component analysis (KPCA) to decrease the input dimensionalities of the model, and build five kinds of model for sleep fidget soft sensor. Simulation results show that these models all have high precision, and it is very easy to select a simple and effective model to implement by hardware. Therefore, the method can not only overcome the correlativity among inputs variables, decrease the dimensionality of the model, but also reduce the complexity of the model, promote the soft sensor model to implement by hardware.
Starting Page	9162
Ending Page	9167
File Size	436686
Page Count	6
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4594379
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Computational modeling Complexity theory Analytical models Kernel Hardware Feature extraction feature extraction soft sensor model complexity sleep fidget parsimonious kernel methods
Content Type	Text
Resource Type	Article

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