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Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where ‘unknown’ faults may occur

Authors:

Yuhua Li,

Michael J. Pont,

N. Barrie JonesAuthors Info & Claims

Pattern Recognition Letters, Volume 23, Issue 5

Pages 569 - 577

https://doi.org/10.1016/S0167-8655(01)00133-7

Published: 01 March 2002 Publication History

Abstract

This paper presents a novel technique which may be used to determine an appropriate threshold for interpreting the outputs of a trained radial basis function (RBF) classifier. Results from two experiments demonstrate that this method can be used to improve the performance of RBF classifiers in practical applications.

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Cited By

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Bezerra CCosta BGuedes LAngelov P(2016)An evolving approach to unsupervised and Real-Time fault detection in industrial processesExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.06.03563:C(134-144)Online publication date: 30-Nov-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.06.035
Barreto GFrota R(2013)A unifying methodology for the evaluation of neural network models on novelty detection tasksPattern Analysis & Applications10.1007/s10044-011-0265-316:1(83-97)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1007/s10044-011-0265-3
Zhang KLi YScarf PBall A(2011)Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networksNeurocomputing10.1016/j.neucom.2011.03.04374:17(2941-2952)Online publication date: 1-Oct-2011
https://dl.acm.org/doi/10.1016/j.neucom.2011.03.043
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Index Terms

Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where ‘unknown’ faults may occur
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

Pattern Recognition Letters Volume 23, Issue 5

March 2002

114 pages

ISSN:0167-8655

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Elsevier Science Inc.

United States

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Published: 01 March 2002

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Bezerra CCosta BGuedes LAngelov P(2016)An evolving approach to unsupervised and Real-Time fault detection in industrial processesExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.06.03563:C(134-144)Online publication date: 30-Nov-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.06.035
Barreto GFrota R(2013)A unifying methodology for the evaluation of neural network models on novelty detection tasksPattern Analysis & Applications10.1007/s10044-011-0265-316:1(83-97)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1007/s10044-011-0265-3
Zhang KLi YScarf PBall A(2011)Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networksNeurocomputing10.1016/j.neucom.2011.03.04374:17(2941-2952)Online publication date: 1-Oct-2011
https://dl.acm.org/doi/10.1016/j.neucom.2011.03.043
Chandola VBanerjee AKumar V(2009)Anomaly detectionACM Computing Surveys10.1145/1541880.154188241:3(1-58)Online publication date: 30-Jul-2009
https://dl.acm.org/doi/10.1145/1541880.1541882
Skubalska-Rafajłowicz E(2008)Random Projection RBF Nets for Multidimensional Density EstimationInternational Journal of Applied Mathematics and Computer Science10.2478/v10006-008-0040-918:4(455-464)Online publication date: 1-Dec-2008
https://dl.acm.org/doi/10.2478/v10006-008-0040-9
Singh SMarkou M(2004)An Approach to Novelty Detection Applied to the Classification of Image RegionsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2004.126966516:4(396-407)Online publication date: 1-Apr-2004
https://dl.acm.org/doi/10.1109/TKDE.2004.1269665
Markou MSingh S(2003)Novelty detectionSignal Processing10.1016/j.sigpro.2003.07.01983:12(2499-2521)Online publication date: 1-Dec-2003
https://dl.acm.org/doi/10.1016/j.sigpro.2003.07.019

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On the versatility of radial basis function neural networks: A case study in the field of intrusion detection

On the construction and training of reformulated radial basis function neural networks

Integrated multiquadric radial basis function approximation methods

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