Comparison of different support vector regression kernels for a servomechanism system

Nnamdi Nwulu

doi:10.1109/ICECDS.2017.8389829

Comparison of different support vector regression kernels for a servomechanism system

Nnamdi Nwulu

Electrical and Electronic Engineering Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This work presents the results from a comparative analysis of various support vector regression kernels that is used to predict the rise time of a servomechanism system. The data for the servomechanism system is obtained from the UCI Machine Learning repository and four input attributes are utilized to predict the rise times for the servomechanism system. The support vector regression kernels investigated are the linear, polynomial, radial basis function and sigmoid functions and the error metrics used for comparison are the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) amongst others. The tenfold cross validation approach is deployed and shows the suitability of the developed systems.

Original language	English
Title of host publication	2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	222-226
Number of pages	5
ISBN (Electronic)	9781538618868
DOIs	https://doi.org/10.1109/ICECDS.2017.8389829
Publication status	Published - 19 Jun 2018
Event	2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017 - Chennai, India Duration: 1 Aug 2017 → 2 Aug 2017

Publication series

Name	2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017

Conference

Conference	2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017
Country/Territory	India
City	Chennai
Period	1/08/17 → 2/08/17

Keywords

Polynomial function
Radial basis function
Servomechanism system
Sigmoid function
Support vector regression

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Information Systems
Decision Sciences (miscellaneous)
Information Systems and Management
Energy Engineering and Power Technology
Computer Science Applications
Signal Processing

Access to Document

10.1109/ICECDS.2017.8389829

Cite this

Nwulu, N. (2018). Comparison of different support vector regression kernels for a servomechanism system. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017 (pp. 222-226). (2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECDS.2017.8389829

Nwulu, Nnamdi. / Comparison of different support vector regression kernels for a servomechanism system. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 222-226 (2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017).

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title = "Comparison of different support vector regression kernels for a servomechanism system",

abstract = "This work presents the results from a comparative analysis of various support vector regression kernels that is used to predict the rise time of a servomechanism system. The data for the servomechanism system is obtained from the UCI Machine Learning repository and four input attributes are utilized to predict the rise times for the servomechanism system. The support vector regression kernels investigated are the linear, polynomial, radial basis function and sigmoid functions and the error metrics used for comparison are the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) amongst others. The tenfold cross validation approach is deployed and shows the suitability of the developed systems.",

keywords = "Polynomial function, Radial basis function, Servomechanism system, Sigmoid function, Support vector regression",

author = "Nnamdi Nwulu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017 ; Conference date: 01-08-2017 Through 02-08-2017",

year = "2018",

month = jun,

day = "19",

doi = "10.1109/ICECDS.2017.8389829",

language = "English",

series = "2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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address = "United States",

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Nwulu, N 2018, Comparison of different support vector regression kernels for a servomechanism system. in 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017, Institute of Electrical and Electronics Engineers Inc., pp. 222-226, 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017, Chennai, India, 1/08/17. https://doi.org/10.1109/ICECDS.2017.8389829

Comparison of different support vector regression kernels for a servomechanism system. / Nwulu, Nnamdi.
2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 222-226 (2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - This work presents the results from a comparative analysis of various support vector regression kernels that is used to predict the rise time of a servomechanism system. The data for the servomechanism system is obtained from the UCI Machine Learning repository and four input attributes are utilized to predict the rise times for the servomechanism system. The support vector regression kernels investigated are the linear, polynomial, radial basis function and sigmoid functions and the error metrics used for comparison are the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) amongst others. The tenfold cross validation approach is deployed and shows the suitability of the developed systems.

AB - This work presents the results from a comparative analysis of various support vector regression kernels that is used to predict the rise time of a servomechanism system. The data for the servomechanism system is obtained from the UCI Machine Learning repository and four input attributes are utilized to predict the rise times for the servomechanism system. The support vector regression kernels investigated are the linear, polynomial, radial basis function and sigmoid functions and the error metrics used for comparison are the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) amongst others. The tenfold cross validation approach is deployed and shows the suitability of the developed systems.

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Nwulu N. Comparison of different support vector regression kernels for a servomechanism system. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 222-226. (2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017). doi: 10.1109/ICECDS.2017.8389829

Comparison of different support vector regression kernels for a servomechanism system

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Keywords

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