Comparison of Neural Network (NN) and Extreme Learning Machine (ELM) on thickness auto adjustment of instant noodle dough on roll press machine

R. Ahsani; A. A. Wardhani; U. F. Nisa; M. Syaiin; R. Y. Adhitya; E. Setiawan; Ii Munadhif; R. A. Atmoko; M. A.P. Negara; A. Soeprijanto; R. T. Soelistijono

doi:10.1109/ISESD.2017.8253314

Comparison of Neural Network (NN) and Extreme Learning Machine (ELM) on thickness auto adjustment of instant noodle dough on roll press machine

R. Ahsani
, A. A. Wardhani
, U. F. Nisa
, M. Syaiin
, R. Y. Adhitya
, E. Setiawan
, Ii Munadhif
, R. A. Atmoko
, M. A.P. Negara
, A. Soeprijanto
, R. T. Soelistijono

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

Abstract

The proposed paper is an auto adjustment of instant noodle dough thickness on a roll press machine. In this plan will be implemented two artificial intelligent methods of Neural Network Back propagation (BP-NN) and Extreme Learning Machine (ELM). The method is intended to improve the performance of the controls on this machine. The input variables are the height and thickness of the dough by using the sharp sensor. While the output plan is two, dc motor and stepper motor. From the results of experiments that have been done, it is concluded that the use of extreme learning machine method is better than the Neural Network method. This is evidenced from the comparison of the average error output value on the ELM of 0.002440673 for training data and 0.6337302 for validation data. While the average error of output value on NN is 0.006638958 for training data and 0.568553072 for validation data. The training time of ELM method is 0.0072812 second and NN method is 0.501274 sec.

Original language	English
Title of host publication	2017 International Symposium on Electronics and Smart Devices, ISESD 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	107-111
Number of pages	5
ISBN (Electronic)	9781538627785
DOIs	https://doi.org/10.1109/ISESD.2017.8253314
Publication status	Published - 1 Jul 2017
Externally published	Yes
Event	2nd International Symposium on Electronics and Smart Devices, ISESD 2017 - Yogyakarta, Indonesia Duration: 17 Oct 2017 → 19 Oct 2017

Publication series

Name	2017 International Symposium on Electronics and Smart Devices, ISESD 2017
Volume	2018-January

Conference

Conference	2nd International Symposium on Electronics and Smart Devices, ISESD 2017
Country/Territory	Indonesia
City	Yogyakarta
Period	17/10/17 → 19/10/17

Keywords

dough height
dough thickness
Extreme Learning Machine
Neural Network
roll press machine

Access to Document

10.1109/ISESD.2017.8253314

Cite this

Ahsani, R., Wardhani, A. A., Nisa, U. F., Syaiin, M., Adhitya, R. Y., Setiawan, E., Munadhif, I., Atmoko, R. A., Negara, M. A. P., Soeprijanto, A., & Soelistijono, R. T. (2017). Comparison of Neural Network (NN) and Extreme Learning Machine (ELM) on thickness auto adjustment of instant noodle dough on roll press machine. In 2017 International Symposium on Electronics and Smart Devices, ISESD 2017 (pp. 107-111). (2017 International Symposium on Electronics and Smart Devices, ISESD 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISESD.2017.8253314

Ahsani, R. ; Wardhani, A. A. ; Nisa, U. F. et al. / Comparison of Neural Network (NN) and Extreme Learning Machine (ELM) on thickness auto adjustment of instant noodle dough on roll press machine. 2017 International Symposium on Electronics and Smart Devices, ISESD 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 107-111 (2017 International Symposium on Electronics and Smart Devices, ISESD 2017).

@inproceedings{c2e68d7a04a846b7a18357c4179290d9,

title = "Comparison of Neural Network (NN) and Extreme Learning Machine (ELM) on thickness auto adjustment of instant noodle dough on roll press machine",

abstract = "The proposed paper is an auto adjustment of instant noodle dough thickness on a roll press machine. In this plan will be implemented two artificial intelligent methods of Neural Network Back propagation (BP-NN) and Extreme Learning Machine (ELM). The method is intended to improve the performance of the controls on this machine. The input variables are the height and thickness of the dough by using the sharp sensor. While the output plan is two, dc motor and stepper motor. From the results of experiments that have been done, it is concluded that the use of extreme learning machine method is better than the Neural Network method. This is evidenced from the comparison of the average error output value on the ELM of 0.002440673 for training data and 0.6337302 for validation data. While the average error of output value on NN is 0.006638958 for training data and 0.568553072 for validation data. The training time of ELM method is 0.0072812 second and NN method is 0.501274 sec.",

keywords = "dough height, dough thickness, Extreme Learning Machine, Neural Network, roll press machine",

author = "R. Ahsani and Wardhani, \{A. A.\} and Nisa, \{U. F.\} and M. Syaiin and Adhitya, \{R. Y.\} and E. Setiawan and Ii Munadhif and Atmoko, \{R. A.\} and Negara, \{M. A.P.\} and A. Soeprijanto and Soelistijono, \{R. T.\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd International Symposium on Electronics and Smart Devices, ISESD 2017 ; Conference date: 17-10-2017 Through 19-10-2017",

year = "2017",

month = jul,

day = "1",

doi = "10.1109/ISESD.2017.8253314",

language = "English",

series = "2017 International Symposium on Electronics and Smart Devices, ISESD 2017",

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

pages = "107--111",

booktitle = "2017 International Symposium on Electronics and Smart Devices, ISESD 2017",

}

Ahsani, R, Wardhani, AA, Nisa, UF, Syaiin, M, Adhitya, RY, Setiawan, E, Munadhif, I, Atmoko, RA, Negara, MAP, Soeprijanto, A & Soelistijono, RT 2017, Comparison of Neural Network (NN) and Extreme Learning Machine (ELM) on thickness auto adjustment of instant noodle dough on roll press machine. in 2017 International Symposium on Electronics and Smart Devices, ISESD 2017. 2017 International Symposium on Electronics and Smart Devices, ISESD 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 107-111, 2nd International Symposium on Electronics and Smart Devices, ISESD 2017, Yogyakarta, Indonesia, 17/10/17. https://doi.org/10.1109/ISESD.2017.8253314

Comparison of Neural Network (NN) and Extreme Learning Machine (ELM) on thickness auto adjustment of instant noodle dough on roll press machine. / Ahsani, R.; Wardhani, A. A.; Nisa, U. F. et al.
2017 International Symposium on Electronics and Smart Devices, ISESD 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 107-111 (2017 International Symposium on Electronics and Smart Devices, ISESD 2017; Vol. 2018-January).

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

TY - GEN

T1 - Comparison of Neural Network (NN) and Extreme Learning Machine (ELM) on thickness auto adjustment of instant noodle dough on roll press machine

AU - Ahsani, R.

AU - Wardhani, A. A.

AU - Nisa, U. F.

AU - Syaiin, M.

AU - Adhitya, R. Y.

AU - Setiawan, E.

AU - Munadhif, Ii

AU - Atmoko, R. A.

AU - Negara, M. A.P.

AU - Soeprijanto, A.

AU - Soelistijono, R. T.

PY - 2017/7/1

Y1 - 2017/7/1

N2 - The proposed paper is an auto adjustment of instant noodle dough thickness on a roll press machine. In this plan will be implemented two artificial intelligent methods of Neural Network Back propagation (BP-NN) and Extreme Learning Machine (ELM). The method is intended to improve the performance of the controls on this machine. The input variables are the height and thickness of the dough by using the sharp sensor. While the output plan is two, dc motor and stepper motor. From the results of experiments that have been done, it is concluded that the use of extreme learning machine method is better than the Neural Network method. This is evidenced from the comparison of the average error output value on the ELM of 0.002440673 for training data and 0.6337302 for validation data. While the average error of output value on NN is 0.006638958 for training data and 0.568553072 for validation data. The training time of ELM method is 0.0072812 second and NN method is 0.501274 sec.

AB - The proposed paper is an auto adjustment of instant noodle dough thickness on a roll press machine. In this plan will be implemented two artificial intelligent methods of Neural Network Back propagation (BP-NN) and Extreme Learning Machine (ELM). The method is intended to improve the performance of the controls on this machine. The input variables are the height and thickness of the dough by using the sharp sensor. While the output plan is two, dc motor and stepper motor. From the results of experiments that have been done, it is concluded that the use of extreme learning machine method is better than the Neural Network method. This is evidenced from the comparison of the average error output value on the ELM of 0.002440673 for training data and 0.6337302 for validation data. While the average error of output value on NN is 0.006638958 for training data and 0.568553072 for validation data. The training time of ELM method is 0.0072812 second and NN method is 0.501274 sec.

KW - dough height

KW - dough thickness

KW - Extreme Learning Machine

KW - Neural Network

KW - roll press machine

UR - https://www.scopus.com/pages/publications/85047564124

U2 - 10.1109/ISESD.2017.8253314

DO - 10.1109/ISESD.2017.8253314

M3 - Conference contribution

AN - SCOPUS:85047564124

T3 - 2017 International Symposium on Electronics and Smart Devices, ISESD 2017

SP - 107

EP - 111

BT - 2017 International Symposium on Electronics and Smart Devices, ISESD 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Symposium on Electronics and Smart Devices, ISESD 2017

Y2 - 17 October 2017 through 19 October 2017

ER -

Ahsani R, Wardhani AA, Nisa UF, Syaiin M, Adhitya RY, Setiawan E et al. Comparison of Neural Network (NN) and Extreme Learning Machine (ELM) on thickness auto adjustment of instant noodle dough on roll press machine. In 2017 International Symposium on Electronics and Smart Devices, ISESD 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 107-111. (2017 International Symposium on Electronics and Smart Devices, ISESD 2017). doi: 10.1109/ISESD.2017.8253314

Comparison of Neural Network (NN) and Extreme Learning Machine (ELM) on thickness auto adjustment of instant noodle dough on roll press machine

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this