Pareto Optimality Concept for Incorporating Prior Knowledge for System Identification Problem with Insufficient Samples

The issue of insufficient samples usually occurs in real engineering problems because of the time-consuming and expensive nature of collecting samples. In general, nonlinear modeling based on limited samples is rather difficult. Incorporating prior knowledge into this type of problem might offer a p...

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Main Authors:	Mohd Ibrahim, Shapiai, Zuwairie, Ibrahim, Asrul, Adam
Format:	Article
Language:	English
Published:	Springer 2017
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/18343/ http://umpir.ump.edu.my/id/eprint/18343/ http://umpir.ump.edu.my/id/eprint/18343/ http://umpir.ump.edu.my/id/eprint/18343/1/pareto%20optimality%20concept.pdf

id	ump-18343
recordtype	eprints
spelling	ump-183432018-01-23T03:33:51Z http://umpir.ump.edu.my/id/eprint/18343/ Pareto Optimality Concept for Incorporating Prior Knowledge for System Identification Problem with Insufficient Samples Mohd Ibrahim, Shapiai Zuwairie, Ibrahim Asrul, Adam TK Electrical engineering. Electronics Nuclear engineering The issue of insufficient samples usually occurs in real engineering problems because of the time-consuming and expensive nature of collecting samples. In general, nonlinear modeling based on limited samples is rather difficult. Incorporating prior knowledge into this type of problem might offer a promising solution. In practice, different forms of prior knowledge may be available, and their use can avoid the weakness of training sample limitation. The primary focus of this study is to introduce an alternative approach for incorporating prior knowledge based on the Pareto optimality concept by improving the initialization of the chromosome and obtaining a reliable Pareto front. In general, the proposed technique relies on the generation of a set of solutions by considering the available training samples and prior knowledge in modeling. As there are many difficulties in obtaining a good Pareto front, we discuss the challenges of implementing the proposed technique, including the formulation of two-objective functions, the uncertainty of the obtained Pareto front and the complexity of the problem space. To validate the proposed technique, a benchmark problem and a control engineering problem are investigated. It is shown that the proposed technique can be implemented by capturing the best solution in the obtained Pareto front, and the accuracy of the prediction for the system identification problem can be improved by up to 10 %. Springer 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/18343/1/pareto%20optimality%20concept.pdf Mohd Ibrahim, Shapiai and Zuwairie, Ibrahim and Asrul, Adam (2017) Pareto Optimality Concept for Incorporating Prior Knowledge for System Identification Problem with Insufficient Samples. Arabian Journal for Science and Engineering, 42 (7). pp. 2697-2710. ISSN 1319-8025 (print); 2191-4281 (online) https://link.springer.com/article/10.1007/s13369-016-2313-1 DOI: 10.1007/s13369-016-2313-1
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 Asrul, Adam Pareto Optimality Concept for Incorporating Prior Knowledge for System Identification Problem with Insufficient Samples
description	The issue of insufficient samples usually occurs in real engineering problems because of the time-consuming and expensive nature of collecting samples. In general, nonlinear modeling based on limited samples is rather difficult. Incorporating prior knowledge into this type of problem might offer a promising solution. In practice, different forms of prior knowledge may be available, and their use can avoid the weakness of training sample limitation. The primary focus of this study is to introduce an alternative approach for incorporating prior knowledge based on the Pareto optimality concept by improving the initialization of the chromosome and obtaining a reliable Pareto front. In general, the proposed technique relies on the generation of a set of solutions by considering the available training samples and prior knowledge in modeling. As there are many difficulties in obtaining a good Pareto front, we discuss the challenges of implementing the proposed technique, including the formulation of two-objective functions, the uncertainty of the obtained Pareto front and the complexity of the problem space. To validate the proposed technique, a benchmark problem and a control engineering problem are investigated. It is shown that the proposed technique can be implemented by capturing the best solution in the obtained Pareto front, and the accuracy of the prediction for the system identification problem can be improved by up to 10 %.
format	Article
author	Mohd Ibrahim, Shapiai Zuwairie, Ibrahim Asrul, Adam
author_facet	Mohd Ibrahim, Shapiai Zuwairie, Ibrahim Asrul, Adam
author_sort	Mohd Ibrahim, Shapiai
title	Pareto Optimality Concept for Incorporating Prior Knowledge for System Identification Problem with Insufficient Samples
title_short	Pareto Optimality Concept for Incorporating Prior Knowledge for System Identification Problem with Insufficient Samples
title_full	Pareto Optimality Concept for Incorporating Prior Knowledge for System Identification Problem with Insufficient Samples
title_fullStr	Pareto Optimality Concept for Incorporating Prior Knowledge for System Identification Problem with Insufficient Samples
title_full_unstemmed	Pareto Optimality Concept for Incorporating Prior Knowledge for System Identification Problem with Insufficient Samples
title_sort	pareto optimality concept for incorporating prior knowledge for system identification problem with insufficient samples
publisher	Springer
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18343/ http://umpir.ump.edu.my/id/eprint/18343/ http://umpir.ump.edu.my/id/eprint/18343/ http://umpir.ump.edu.my/id/eprint/18343/1/pareto%20optimality%20concept.pdf
first_indexed	2023-09-18T22:25:56Z
last_indexed	2023-09-18T22:25:56Z
_version_	1777415966235295744

Pareto Optimality Concept for Incorporating Prior Knowledge for System Identification Problem with Insufficient Samples

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