Extreme learning machine and back propagation neural network comparison for temperature and humidity control of oyster mushroom based on microcontroller

G. M. Fuady; A. H. Turoobi; M. N. Majdi; M. Syaiin; R. Y. Adhitya; Isa Rachman; F. Rachman; M. A.P. Negara; A. Soeprijanto; R. T. Soelistijono

doi:10.1109/ISESD.2017.8253303

Extreme learning machine and back propagation neural network comparison for temperature and humidity control of oyster mushroom based on microcontroller

G. M. Fuady, A. H. Turoobi, M. N. Majdi, M. Syaiin, R. Y. Adhitya, Isa Rachman, F. Rachman, M. A.P. Negara, A. Soeprijanto, R. T. Soelistijono

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

12 Citations (Scopus)

Abstract

This paper presents design and experimental studies of Extreme Learning Machine (ELM) to control temperature and humidity of oyster mushroom farm house. The ideal temperature to optimize the growth of oyster mushroom in low lying areas is for about 28° Celsius and 80% of humidity, while the current method for controlling temperature and humidity is done by conventional manner using manual sprayer. Given these problems, a Single Layer Feed Forward Neural Network (SLFN's) with modification of H inverse matrix versus target matrix or also known as ELM can control the temperature and humidity of oyster mushroom farm house more faster and effectively than previous research. DHT11 sensor is used to read the temperature and humidity value. Exhaust fan and mist maker are used for conditioning the control variables. Several beginning conditions were built to compare ELM with previous methods such back propagation neural network and zero order FLC in term to find the suitable methodfor this problem.

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	46-50
Number of pages	5
ISBN (Electronic)	9781538627785
DOIs	https://doi.org/10.1109/ISESD.2017.8253303
Publication status	Published - 1 Jul 2017
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

DHT11
Extreme Learning Machine
Intel Galileo
Oyster Mushroom

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10.1109/ISESD.2017.8253303

Cite this

Fuady, G. M., Turoobi, A. H., Majdi, M. N., Syaiin, M., Adhitya, R. Y., Rachman, I., Rachman, F., Negara, M. A. P., Soeprijanto, A., & Soelistijono, R. T. (2017). Extreme learning machine and back propagation neural network comparison for temperature and humidity control of oyster mushroom based on microcontroller. In 2017 International Symposium on Electronics and Smart Devices, ISESD 2017 (pp. 46-50). (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.2017.8253303

Fuady, G. M. ; Turoobi, A. H. ; Majdi, M. N. et al. / Extreme learning machine and back propagation neural network comparison for temperature and humidity control of oyster mushroom based on microcontroller. 2017 International Symposium on Electronics and Smart Devices, ISESD 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 46-50 (2017 International Symposium on Electronics and Smart Devices, ISESD 2017).

@inproceedings{01182016325c4c248b429a7aba6a8485,

title = "Extreme learning machine and back propagation neural network comparison for temperature and humidity control of oyster mushroom based on microcontroller",

abstract = "This paper presents design and experimental studies of Extreme Learning Machine (ELM) to control temperature and humidity of oyster mushroom farm house. The ideal temperature to optimize the growth of oyster mushroom in low lying areas is for about 28° Celsius and 80% of humidity, while the current method for controlling temperature and humidity is done by conventional manner using manual sprayer. Given these problems, a Single Layer Feed Forward Neural Network (SLFN's) with modification of H inverse matrix versus target matrix or also known as ELM can control the temperature and humidity of oyster mushroom farm house more faster and effectively than previous research. DHT11 sensor is used to read the temperature and humidity value. Exhaust fan and mist maker are used for conditioning the control variables. Several beginning conditions were built to compare ELM with previous methods such back propagation neural network and zero order FLC in term to find the suitable methodfor this problem.",

keywords = "DHT11, Extreme Learning Machine, Intel Galileo, Oyster Mushroom",

author = "Fuady, {G. M.} and Turoobi, {A. H.} and Majdi, {M. N.} and M. Syaiin and Adhitya, {R. Y.} and Isa Rachman and F. Rachman 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.2017.8253303",

language = "English",

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

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

pages = "46--50",

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}

Fuady, GM, Turoobi, AH, Majdi, MN, Syaiin, M, Adhitya, RY, Rachman, I, Rachman, F, Negara, MAP, Soeprijanto, A & Soelistijono, RT 2017, Extreme learning machine and back propagation neural network comparison for temperature and humidity control of oyster mushroom based on microcontroller. 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. 46-50, 2nd International Symposium on Electronics and Smart Devices, ISESD 2017, Yogyakarta, Indonesia, 17/10/17. https://doi.org/10.1109/ISESD.2017.8253303

Extreme learning machine and back propagation neural network comparison for temperature and humidity control of oyster mushroom based on microcontroller. / Fuady, G. M.; Turoobi, A. H.; Majdi, M. N. et al.
2017 International Symposium on Electronics and Smart Devices, ISESD 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 46-50 (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

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T1 - Extreme learning machine and back propagation neural network comparison for temperature and humidity control of oyster mushroom based on microcontroller

AU - Fuady, G. M.

AU - Turoobi, A. H.

AU - Majdi, M. N.

AU - Syaiin, M.

AU - Adhitya, R. Y.

AU - Rachman, Isa

AU - Rachman, F.

AU - Negara, M. A.P.

AU - Soeprijanto, A.

AU - Soelistijono, R. T.

PY - 2017/7/1

Y1 - 2017/7/1

N2 - This paper presents design and experimental studies of Extreme Learning Machine (ELM) to control temperature and humidity of oyster mushroom farm house. The ideal temperature to optimize the growth of oyster mushroom in low lying areas is for about 28° Celsius and 80% of humidity, while the current method for controlling temperature and humidity is done by conventional manner using manual sprayer. Given these problems, a Single Layer Feed Forward Neural Network (SLFN's) with modification of H inverse matrix versus target matrix or also known as ELM can control the temperature and humidity of oyster mushroom farm house more faster and effectively than previous research. DHT11 sensor is used to read the temperature and humidity value. Exhaust fan and mist maker are used for conditioning the control variables. Several beginning conditions were built to compare ELM with previous methods such back propagation neural network and zero order FLC in term to find the suitable methodfor this problem.

AB - This paper presents design and experimental studies of Extreme Learning Machine (ELM) to control temperature and humidity of oyster mushroom farm house. The ideal temperature to optimize the growth of oyster mushroom in low lying areas is for about 28° Celsius and 80% of humidity, while the current method for controlling temperature and humidity is done by conventional manner using manual sprayer. Given these problems, a Single Layer Feed Forward Neural Network (SLFN's) with modification of H inverse matrix versus target matrix or also known as ELM can control the temperature and humidity of oyster mushroom farm house more faster and effectively than previous research. DHT11 sensor is used to read the temperature and humidity value. Exhaust fan and mist maker are used for conditioning the control variables. Several beginning conditions were built to compare ELM with previous methods such back propagation neural network and zero order FLC in term to find the suitable methodfor this problem.

KW - DHT11

KW - Extreme Learning Machine

KW - Intel Galileo

KW - Oyster Mushroom

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M3 - Conference contribution

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

SP - 46

EP - 50

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 -

Fuady GM, Turoobi AH, Majdi MN, Syaiin M, Adhitya RY, Rachman I et al. Extreme learning machine and back propagation neural network comparison for temperature and humidity control of oyster mushroom based on microcontroller. In 2017 International Symposium on Electronics and Smart Devices, ISESD 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 46-50. (2017 International Symposium on Electronics and Smart Devices, ISESD 2017). doi: 10.1109/ISESD.2017.8253303

Extreme learning machine and back propagation neural network comparison for temperature and humidity control of oyster mushroom based on microcontroller

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