NDLI: Training data reduction for nonlinear state estimator

Content Provider	IEEE Xplore Digital Library
Author	Ishiyama, H. Yamakita, M.
Copyright Year	2015
Description	Author affiliation: Dept. of Mech. & Control Eng., Tokyo Inst. of Technol., Tokyo, Japan (Ishiyama, H.; Yamakita, M.)
Abstract	In this paper, an approach for speeding up a kernel based nonlinear state estimator is proposed. The kernel based observer, which we are going to speed up in this paper, is one of the state estimator which employs a non-parametric structure. Although it shows high precision for nonlinear estimation due to its nonlinear nature, large amount of calculation makes it rather slow. In this paper, we propose some speeding up methods for a kernel based observer. Our speeding up methods are based on the data reduction of training data, which is stock in the memory of observer in a huge amount. By selecting and reducing those training data carefully, we show that we can reduce those training data with minimum degradations. We propose two types of data reduction strategies. First one is a method using a coefficient matrix which is easily induced from the kernel estimation method. In this method the coefficient matrix is processed to show that it is a mimic index of the likelihood which is indicating the importance of each training data. The second one is a method applying sparse algorithm. In this method the data reduction process is reduced to a multiplication of sparse diagonal matrix. The sparse diagonal matrix is calculated by a sparsification algorithm, where it is possible not only to avoid solving combinatorial optimization problems, but also to reduce the data in a deterministic way. Both strategies are tested in numerical simulations.
Starting Page	1
Ending Page	4
File Size	169377
Page Count	4
File Format	PDF
ISBN	9781479978625
DOI	10.1109/ASCC.2015.7244669
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-31
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Training data Estimation Numerical models Sparse matrices Mathematical model Kernel
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in