Sensor fusion and nonlinear prediction for anomalous event detection

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The authors consider the problem of using the information from various time series, each one characterizing a different physical quantity, to predict the future state of the system and, based on that information, to detect and classify anomalous events. They stress the application of principal components analysis (PCA) to analyze and combine data from different sensors. They construct both linear and nonlinear predictors. In particular, for linear prediction the authors use the least-mean-square (LMS) algorithm and for nonlinear prediction they use both backpropagation (BP) networks and fuzzy predictors (FP). As an application, they consider the prediction of gamma counts from ... continued below

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15 p.

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Hernandez, J.V.; Moore, K.R. & Elphic, R.C. March 7, 1995.

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Description

The authors consider the problem of using the information from various time series, each one characterizing a different physical quantity, to predict the future state of the system and, based on that information, to detect and classify anomalous events. They stress the application of principal components analysis (PCA) to analyze and combine data from different sensors. They construct both linear and nonlinear predictors. In particular, for linear prediction the authors use the least-mean-square (LMS) algorithm and for nonlinear prediction they use both backpropagation (BP) networks and fuzzy predictors (FP). As an application, they consider the prediction of gamma counts from past values of electron and gamma counts recorded by the instruments of a high altitude satellite.

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15 p.

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OSTI as DE95009429

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  • SPIE international symposium on aerospace/defense sensing and dual-use photonics, Orlando, FL (United States), 17-21 Apr 1995

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  • Other: DE95009429
  • Report No.: LA-UR--95-883
  • Report No.: CONF-950472--4
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 42475
  • Archival Resource Key: ark:/67531/metadc684442

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  • March 7, 1995

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  • July 25, 2015, 2:20 a.m.

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  • Feb. 26, 2016, 4:37 p.m.

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Hernandez, J.V.; Moore, K.R. & Elphic, R.C. Sensor fusion and nonlinear prediction for anomalous event detection, article, March 7, 1995; New Mexico. (digital.library.unt.edu/ark:/67531/metadc684442/: accessed June 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.