Algorithm for electrical parameters and payload based on neural network

Wei Jiang Wang; Yue Ting Shi; Jian Yan Liu; Gui Shan Ren; Han Yuan Dong

doi:10.15918/j.tbit1001-0645.2015.07.010

Algorithm for electrical parameters and payload based on neural network

Wei Jiang Wang
, Yue Ting Shi^*
, Jian Yan Liu
, Gui Shan Ren
, Han Yuan Dong

^*Corresponding author for this work

School of Integrated Circuits and Electronics

Beijing Institute of Technology
China National Petroleum Corporation

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

A BP neural network based algorithm for payload calculation was proposed. A high dimensional input-output system was formed to describe the non-linear relation between electrical parameters and payload. By the use of negative gradient of the network error feedback, network parameters were automatically adjusted, therefore the mapping connection between electrical parameters and payload can be solved. Experimental results and simulations show that the algorithm can solve the problem of the non-linear approximation, and achieve an error probability of e-3 with correlation coefficient up to 0.95.

Original language	English
Pages (from-to)	706-710
Number of pages	5
Journal	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume	35
Issue number	7
DOIs	https://doi.org/10.15918/j.tbit1001-0645.2015.07.010
Publication status	Published - 1 Jul 2015

Keywords

BP neural network
Dynamometer card
Electrical parameters
Rod payload

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10.15918/j.tbit1001-0645.2015.07.010

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abstract = "A BP neural network based algorithm for payload calculation was proposed. A high dimensional input-output system was formed to describe the non-linear relation between electrical parameters and payload. By the use of negative gradient of the network error feedback, network parameters were automatically adjusted, therefore the mapping connection between electrical parameters and payload can be solved. Experimental results and simulations show that the algorithm can solve the problem of the non-linear approximation, and achieve an error probability of e-3 with correlation coefficient up to 0.95.",

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AU - Wang, Wei Jiang

AU - Shi, Yue Ting

AU - Liu, Jian Yan

AU - Ren, Gui Shan

AU - Dong, Han Yuan

PY - 2015/7/1

Y1 - 2015/7/1

N2 - A BP neural network based algorithm for payload calculation was proposed. A high dimensional input-output system was formed to describe the non-linear relation between electrical parameters and payload. By the use of negative gradient of the network error feedback, network parameters were automatically adjusted, therefore the mapping connection between electrical parameters and payload can be solved. Experimental results and simulations show that the algorithm can solve the problem of the non-linear approximation, and achieve an error probability of e-3 with correlation coefficient up to 0.95.

AB - A BP neural network based algorithm for payload calculation was proposed. A high dimensional input-output system was formed to describe the non-linear relation between electrical parameters and payload. By the use of negative gradient of the network error feedback, network parameters were automatically adjusted, therefore the mapping connection between electrical parameters and payload can be solved. Experimental results and simulations show that the algorithm can solve the problem of the non-linear approximation, and achieve an error probability of e-3 with correlation coefficient up to 0.95.

KW - BP neural network

KW - Dynamometer card

KW - Electrical parameters

KW - Rod payload

UR - https://www.scopus.com/pages/publications/84944328323

U2 - 10.15918/j.tbit1001-0645.2015.07.010

DO - 10.15918/j.tbit1001-0645.2015.07.010

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JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

IS - 7

ER -

Algorithm for electrical parameters and payload based on neural network

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Keywords

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