Application of neural networks for gross error detection

Industrial & Engineering Chemistry Research ◽

10.1021/ie00020a017 ◽

1993 ◽

Vol 32 (8) ◽

pp. 1651-1657 ◽

Author(s):

Gagan Gupta ◽

Shankar Narasimhan

Keyword(s):

Neural Networks ◽

Error Detection ◽

Gross Error Detection ◽

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Data rectification and gross error detection in a steady state process via artificial neural networks

Industrial & Engineering Chemistry Research ◽

10.1021/ie00024a010 ◽

1993 ◽

Vol 32 (12) ◽

pp. 3020-3028 ◽

Author(s):

Patricia A. Terry ◽

David M. Himmelblau

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Steady State ◽

Error Detection ◽

Gross Error Detection ◽

Data Rectification ◽

State Process ◽

Gross Error ◽

Artificial Neural ◽

Steady State Process

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Application of data reconciliation and gross error detection techniques to enhance reliability and consistency of the blast furnace process data

Asia-Pacific Journal of Chemical Engineering ◽

10.1002/apj.2628 ◽

2021 ◽

Author(s):

Sujan Hazra ◽

Prakash Abhale ◽

Samik Nag ◽

Sam Mathew ◽

Shankar Narasimhan

Keyword(s):

Blast Furnace ◽

Error Detection ◽

Data Reconciliation ◽

Blast Furnace Process ◽

Process Data ◽

Gross Error Detection ◽

Furnace Process ◽

Detection Techniques ◽

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A Review on Data Reconciliation and Gross Error Detection for Process Plant Energy Management

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1051/1/012052 ◽

2021 ◽

Vol 1051 (1) ◽

pp. 012052

Author(s):

A S A Wahab ◽

P Y Liew ◽

N A Manaf

Keyword(s):

Energy Management ◽

Error Detection ◽

Data Reconciliation ◽

Gross Error Detection ◽

Gross Error ◽

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An improved MT–NT method for gross error detection and data reconciliation

Computers & Chemical Engineering ◽

10.1016/j.compchemeng.2004.04.003 ◽

2004 ◽

Vol 28 (11) ◽

pp. 2189-2192 ◽

Author(s):

Fang Wang ◽

Xiao-ping Jia ◽

Shi-qing Zheng ◽

Jin-cai Yue

Keyword(s):

Error Detection ◽

Data Reconciliation ◽

Gross Error Detection ◽

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Gross error detection when variance-covariance matrices are unknown

AIChE Journal ◽

10.1002/aic.690390810 ◽

1993 ◽

Vol 39 (8) ◽

pp. 1335-1341 ◽

Author(s):

D. K. Rollins ◽

J. F. Davis

Keyword(s):

Error Detection ◽

Covariance Matrices ◽

Gross Error Detection ◽

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A Strategy for Gross-Error Detection in Satellite Altimeter Data Applied in the Baltic-Sea Area for Enhanced Geoid and Gravity Determination

International Association of Geodesy Symposia - Determination of the Geoid ◽

10.1007/978-1-4612-3104-2_12 ◽

1991 ◽

pp. 95-107 ◽

Author(s):

C. C. Tscherning

Keyword(s):

Error Detection ◽

Altimeter Data ◽

The Baltic Sea ◽

Satellite Altimeter ◽

Gross Error Detection ◽

Gross Error ◽

Sea Area ◽

Satellite Altimeter Data

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Applications of graph theory to gross error detection for GPS geodetic control networks

Geo-spatial Information Science ◽

10.1007/bf02826471 ◽

2002 ◽

Vol 5 (4) ◽

pp. 26-31 ◽

Author(s):

Samwel Katambi ◽

Guo Jiming ◽

Kong Xiangyuan

Keyword(s):

Graph Theory ◽

Error Detection ◽

Gross Error Detection ◽

Control Networks ◽

Gross Error ◽

Geodetic Control

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Industrial Applications Using Wavelet Packets for Gross Error Detection

Studies in Computational Intelligence - Computational Intelligence in Information Assurance and Security ◽

10.1007/978-3-540-71078-3_4 ◽

2007 ◽

pp. 89-127 ◽

Author(s):

Paolo Mercorelli ◽

Alexander Frick

Keyword(s):

Error Detection ◽

Industrial Applications ◽

Wavelet Packets ◽

Gross Error Detection ◽

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A Practical Study of Gross-Error Detection in Bundle Adjustment

The Canadian Surveyor ◽

10.1139/tcs-1981-0053 ◽

1981 ◽

Vol 35 (4) ◽

pp. 373-386 ◽

Author(s):

S. F. El-Hakim

Keyword(s):

Error Detection ◽

Bundle Adjustment ◽

Image Point ◽

Gross Error Detection ◽

Factors Affecting ◽

Gross Error ◽

Adjustment Program ◽

Error Detector ◽

Additional Constraints ◽

A special bundle adjustment program has been developed with a built-in automatic gross-error detector which uses the data snooping approach. The program computes, in an efficient way, the exact value of the redundancy numbers for each image point. Using actual data, many of the factors affecting the reliability of bundle adjustment and the ability of the technique to detect gross errors are studied. The effects of different block parameters, point type and location, and the use of additional constraints are presented.

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Correntropy based data reconciliation and gross error detection for bilinear systems

Chemical Engineering Science ◽

10.1016/j.ces.2019.115327 ◽

2020 ◽

Vol 212 ◽

pp. 115327 ◽

Author(s):

Zhengjiang Zhang ◽

Lester Lik Teck Chan ◽

Junghui Chen ◽

Zhijiang Shao

Keyword(s):

Error Detection ◽

Bilinear Systems ◽

Data Reconciliation ◽

Gross Error Detection ◽

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