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Modelling and prediction of surface roughness and power consumption using parallel extreme learning machine based particle swarm optimization

Prediction model allows the machinist to determine the values of the cutting performance before machining. Modelling using improved extreme learning machine based particle swarm optimization, IPSO-ELM has less parameters to adjust and also takes real number as particles while decreasing the norm of...

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Main Authors: Nooraziah Ahmad, Tiagrajah V. Janahiraman
格式: Book Section
出版: 2014
在線閱讀:http://discol.umk.edu.my/id/eprint/8600/
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spelling my.umk.eprints.86002022-05-23T10:38:18Z http://discol.umk.edu.my/id/eprint/8600/ Modelling and prediction of surface roughness and power consumption using parallel extreme learning machine based particle swarm optimization Nooraziah Ahmad Tiagrajah V. Janahiraman Prediction model allows the machinist to determine the values of the cutting performance before machining. Modelling using improved extreme learning machine based particle swarm optimization, IPSO-ELM has less parameters to adjust and also takes real number as particles while decreasing the norm of output weights and constraining the input weight and hidden biases within a reasonable range to improve the ELM performance. In order to solve the multi objectives modelling problem, we have proposed a parallel IPSO-ELM. In this research work, the best input weights and hidden biases for different performance were identified. The proposed method was able to model the training and the testing set with minimal error. The predicted result from the designed model was able to match the experimental data very closely. 2014 Book Section NonPeerReviewed Nooraziah Ahmad and Tiagrajah V. Janahiraman (2014) Modelling and prediction of surface roughness and power consumption using parallel extreme learning machine based particle swarm optimization. In: Proceeding of ELM 2014 Volume 2. UNSPECIFIED, pp. 321-322. ISBN 978331914066-7
institution Universiti Malaysia Kelantan
building Perpustakaan Universiti Malaysia Kelantan
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Kelantan
content_source UMK Institutional Repository
url_provider http://umkeprints.umk.edu.my/
description Prediction model allows the machinist to determine the values of the cutting performance before machining. Modelling using improved extreme learning machine based particle swarm optimization, IPSO-ELM has less parameters to adjust and also takes real number as particles while decreasing the norm of output weights and constraining the input weight and hidden biases within a reasonable range to improve the ELM performance. In order to solve the multi objectives modelling problem, we have proposed a parallel IPSO-ELM. In this research work, the best input weights and hidden biases for different performance were identified. The proposed method was able to model the training and the testing set with minimal error. The predicted result from the designed model was able to match the experimental data very closely.
format Book Section
author Nooraziah Ahmad
Tiagrajah V. Janahiraman
spellingShingle Nooraziah Ahmad
Tiagrajah V. Janahiraman
Modelling and prediction of surface roughness and power consumption using parallel extreme learning machine based particle swarm optimization
author_facet Nooraziah Ahmad
Tiagrajah V. Janahiraman
author_sort Nooraziah Ahmad
title Modelling and prediction of surface roughness and power consumption using parallel extreme learning machine based particle swarm optimization
title_short Modelling and prediction of surface roughness and power consumption using parallel extreme learning machine based particle swarm optimization
title_full Modelling and prediction of surface roughness and power consumption using parallel extreme learning machine based particle swarm optimization
title_fullStr Modelling and prediction of surface roughness and power consumption using parallel extreme learning machine based particle swarm optimization
title_full_unstemmed Modelling and prediction of surface roughness and power consumption using parallel extreme learning machine based particle swarm optimization
title_sort modelling and prediction of surface roughness and power consumption using parallel extreme learning machine based particle swarm optimization
publishDate 2014
url http://discol.umk.edu.my/id/eprint/8600/
_version_ 1763304007444463616
score 13.250246

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