Enhanced weighted kernel regression with prior knowledge using robot manipulator problem as a case study

Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. In general, WKR has proven to be effective when learning from small samples as compared to artificial neural network with back-propagation (ANNBP) and some other techniques. In order to extend the capab...

Full description

Bibliographic Details
Main Authors:	Mohd Ibrahim, Shapiai, Zuwairie, Ibrahim, Marzuki, Khalid
Format:	Article
Language:	English
Published:	Elsevier Ltd 2012
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/25325/ http://umpir.ump.edu.my/id/eprint/25325/ http://umpir.ump.edu.my/id/eprint/25325/ http://umpir.ump.edu.my/id/eprint/25325/1/Enhanced%20weighted%20kernel%20regression%20with%20prior%20knowledge%20using%20robot.pdf

id	ump-25325
recordtype	eprints
spelling	ump-253252019-12-10T01:06:04Z http://umpir.ump.edu.my/id/eprint/25325/ Enhanced weighted kernel regression with prior knowledge using robot manipulator problem as a case study Mohd Ibrahim, Shapiai Zuwairie, Ibrahim Marzuki, Khalid TK Electrical engineering. Electronics Nuclear engineering Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. In general, WKR has proven to be effective when learning from small samples as compared to artificial neural network with back-propagation (ANNBP) and some other techniques. In order to extend the capability of the technique, we introduce a new approach to improve the WKR by incorporating the prior knowledge. In practice, different forms of prior knowledge may be available and it might avoid the weakness of the training samples limitation. In this study, the incorporation of the prior knowledge will produce a set of solutions by considering the available training samples and prior knowledge in modeling. The process involved in obtaining a set of solutions can be regarded as a bi-objective optimization problem. The proposed technique is derived based on the pareto optimality concept (POC) by using multi-objective optimization technique (MOPT). We only focus the study on the challenges of formulating the two objective functions. We demonstrate the capability of the proposed technique to robot manipulator problem. It is shown that the incorporation of the prior knowledge based on POC can be implemented and relatively improved the regression performance. Some related issues of the proposed technique are also discussed. Elsevier Ltd 2012 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25325/1/Enhanced%20weighted%20kernel%20regression%20with%20prior%20knowledge%20using%20robot.pdf Mohd Ibrahim, Shapiai and Zuwairie, Ibrahim and Marzuki, Khalid (2012) Enhanced weighted kernel regression with prior knowledge using robot manipulator problem as a case study. Procedia Engineering, 41. pp. 82-89. ISSN 1877-7058 https://doi.org/10.1016/j.proeng.2012.07.146 https://doi.org/10.1016/j.proeng.2012.07.146
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Mohd Ibrahim, Shapiai Zuwairie, Ibrahim Marzuki, Khalid Enhanced weighted kernel regression with prior knowledge using robot manipulator problem as a case study
description	Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. In general, WKR has proven to be effective when learning from small samples as compared to artificial neural network with back-propagation (ANNBP) and some other techniques. In order to extend the capability of the technique, we introduce a new approach to improve the WKR by incorporating the prior knowledge. In practice, different forms of prior knowledge may be available and it might avoid the weakness of the training samples limitation. In this study, the incorporation of the prior knowledge will produce a set of solutions by considering the available training samples and prior knowledge in modeling. The process involved in obtaining a set of solutions can be regarded as a bi-objective optimization problem. The proposed technique is derived based on the pareto optimality concept (POC) by using multi-objective optimization technique (MOPT). We only focus the study on the challenges of formulating the two objective functions. We demonstrate the capability of the proposed technique to robot manipulator problem. It is shown that the incorporation of the prior knowledge based on POC can be implemented and relatively improved the regression performance. Some related issues of the proposed technique are also discussed.
format	Article
author	Mohd Ibrahim, Shapiai Zuwairie, Ibrahim Marzuki, Khalid
author_facet	Mohd Ibrahim, Shapiai Zuwairie, Ibrahim Marzuki, Khalid
author_sort	Mohd Ibrahim, Shapiai
title	Enhanced weighted kernel regression with prior knowledge using robot manipulator problem as a case study
title_short	Enhanced weighted kernel regression with prior knowledge using robot manipulator problem as a case study
title_full	Enhanced weighted kernel regression with prior knowledge using robot manipulator problem as a case study
title_fullStr	Enhanced weighted kernel regression with prior knowledge using robot manipulator problem as a case study
title_full_unstemmed	Enhanced weighted kernel regression with prior knowledge using robot manipulator problem as a case study
title_sort	enhanced weighted kernel regression with prior knowledge using robot manipulator problem as a case study
publisher	Elsevier Ltd
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/25325/ http://umpir.ump.edu.my/id/eprint/25325/ http://umpir.ump.edu.my/id/eprint/25325/ http://umpir.ump.edu.my/id/eprint/25325/1/Enhanced%20weighted%20kernel%20regression%20with%20prior%20knowledge%20using%20robot.pdf
first_indexed	2023-09-18T22:38:50Z
last_indexed	2023-09-18T22:38:50Z
_version_	1777416777740845056

Enhanced weighted kernel regression with prior knowledge using robot manipulator problem as a case study

Similar Items