Cover Image

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...

Full description

Saved in:
Bibliographic Details
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/18343/1/pareto%20optimality%20concept.pdf
http://umpir.ump.edu.my/id/eprint/18343/
https://link.springer.com/article/10.1007/s13369-016-2313-1
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.18343
record_format eprints
spelling my.ump.umpir.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
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/18343/1/pareto%20optimality%20concept.pdf
http://umpir.ump.edu.my/id/eprint/18343/
https://link.springer.com/article/10.1007/s13369-016-2313-1
_version_ 1643668421542412288
score 13.211869

Similar Items