Weight Initialization on Neural Network for Neuro PID Controller: Case study

Theertham Akilesh Sai, HeeHyol Lee

研究成果: Conference contribution

抄録

Neuro PID controller has been widely used in control field in recent times. Random weight initialization is used in the Neuro PID controller. The impact of various weight initialization has not been studied in the Neuro PID controller. The weight initialization methods such as Xavier initialization and He initialization have been proven to be effective in faster convergence in neural network. This paper investigated a weight initialization concept in Neuro PID controller by case studying with zero initialization, constant initialization, Gaussian distributed initialization, uniform distributed initialization, He initialization, and Xavier initialization in typical first-order lag elements, integrator elements, and dead time elements to obtain suitable initialization of weight coefficients, which reduces settling time for the neural network.

元の言語English
ホスト出版物のタイトル2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018
出版者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728119960
DOI
出版物ステータスPublished - 2018 11 27
イベント2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018 - Busan, Korea, Republic of
継続期間: 2018 9 62018 9 8

Other

Other2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018
Korea, Republic of
Busan
期間18/9/618/9/8

Fingerprint

PID Controller
Initialization
neural network
Neural Networks
Neural networks
Controllers
settling
time
Weight Coefficient

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Communication

これを引用

Sai, T. A., & Lee, H. (2018). Weight Initialization on Neural Network for Neuro PID Controller: Case study. : 2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018 [8549904] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICT-ROBOT.2018.8549904

Weight Initialization on Neural Network for Neuro PID Controller : Case study. / Sai, Theertham Akilesh; Lee, HeeHyol.

2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8549904.

研究成果: Conference contribution

Sai, TA & Lee, H 2018, Weight Initialization on Neural Network for Neuro PID Controller: Case study. : 2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018., 8549904, Institute of Electrical and Electronics Engineers Inc., 2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018, Busan, Korea, Republic of, 18/9/6. https://doi.org/10.1109/ICT-ROBOT.2018.8549904
Sai TA, Lee H. Weight Initialization on Neural Network for Neuro PID Controller: Case study. : 2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8549904 https://doi.org/10.1109/ICT-ROBOT.2018.8549904
Sai, Theertham Akilesh ; Lee, HeeHyol. / Weight Initialization on Neural Network for Neuro PID Controller : Case study. 2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018. Institute of Electrical and Electronics Engineers Inc., 2018.
@inproceedings{6a54557e7cde4712b96f10f782e5ee6b,
title = "Weight Initialization on Neural Network for Neuro PID Controller: Case study",
abstract = "Neuro PID controller has been widely used in control field in recent times. Random weight initialization is used in the Neuro PID controller. The impact of various weight initialization has not been studied in the Neuro PID controller. The weight initialization methods such as Xavier initialization and He initialization have been proven to be effective in faster convergence in neural network. This paper investigated a weight initialization concept in Neuro PID controller by case studying with zero initialization, constant initialization, Gaussian distributed initialization, uniform distributed initialization, He initialization, and Xavier initialization in typical first-order lag elements, integrator elements, and dead time elements to obtain suitable initialization of weight coefficients, which reduces settling time for the neural network.",
keywords = "Gaussian distributed initialization, He initialization, uniform distributed initialization, Xavier initialization, zero initialization",
author = "Sai, {Theertham Akilesh} and HeeHyol Lee",
year = "2018",
month = "11",
day = "27",
doi = "10.1109/ICT-ROBOT.2018.8549904",
language = "English",
booktitle = "2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Weight Initialization on Neural Network for Neuro PID Controller

T2 - Case study

AU - Sai, Theertham Akilesh

AU - Lee, HeeHyol

PY - 2018/11/27

Y1 - 2018/11/27

N2 - Neuro PID controller has been widely used in control field in recent times. Random weight initialization is used in the Neuro PID controller. The impact of various weight initialization has not been studied in the Neuro PID controller. The weight initialization methods such as Xavier initialization and He initialization have been proven to be effective in faster convergence in neural network. This paper investigated a weight initialization concept in Neuro PID controller by case studying with zero initialization, constant initialization, Gaussian distributed initialization, uniform distributed initialization, He initialization, and Xavier initialization in typical first-order lag elements, integrator elements, and dead time elements to obtain suitable initialization of weight coefficients, which reduces settling time for the neural network.

AB - Neuro PID controller has been widely used in control field in recent times. Random weight initialization is used in the Neuro PID controller. The impact of various weight initialization has not been studied in the Neuro PID controller. The weight initialization methods such as Xavier initialization and He initialization have been proven to be effective in faster convergence in neural network. This paper investigated a weight initialization concept in Neuro PID controller by case studying with zero initialization, constant initialization, Gaussian distributed initialization, uniform distributed initialization, He initialization, and Xavier initialization in typical first-order lag elements, integrator elements, and dead time elements to obtain suitable initialization of weight coefficients, which reduces settling time for the neural network.

KW - Gaussian distributed initialization

KW - He initialization

KW - uniform distributed initialization

KW - Xavier initialization

KW - zero initialization

UR - http://www.scopus.com/inward/record.url?scp=85060024091&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85060024091&partnerID=8YFLogxK

U2 - 10.1109/ICT-ROBOT.2018.8549904

DO - 10.1109/ICT-ROBOT.2018.8549904

M3 - Conference contribution

AN - SCOPUS:85060024091

BT - 2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -