Uncertainty quantification methods for neural networks pattern recognition

Silvia Tolo, T. V. Santhosh, Gopika Vinod, Uchenna Oparaji, Edoardo Patelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

2 Citations (Scopus)

Abstract

On-line monitoring techniques have attracted increasing attention as a promising strategy for improving safety, maintaining availability and reducing the cost of operation and maintenance. In particular, pattern recognition tools such as artificial neural networks are today largely adopted for sensor validation, plant component monitoring, system control, and fault-diagnostics based on the data acquired during operation. However, classic artificial neural networks do not provide an error context for the model response, whose robustness remains thus difficult to estimate. Indeed, experimental data generally exhibit a time/space-varying behaviour and are hence characterized by an intrinsic level of uncertainty that unavoidably affects the performance of the tools adopted and undermines the accuracy of the analysis. For this reason, the propagation of the uncertainty and the quantification of the so called margins of uncertainty in output are crucial in making risk-informed decision. The current study presents a comparison between two different approaches for the quantification of uncertainty in artificial neural networks. The first technique presented is based on the error estimation by a series association scheme, the second approach couples Bayesian model selection technique and model averaging into a unified framework. The efficiency of these two approaches are analysed in terms of their computational cost and predictive performance, through their application to a nuclear power plant fault diagnosis system.

Original languageEnglish
Title of host publication2017 IEEE Symposium Series on Computational Intelligence
Place of PublicationPiscataway, NJ
Number of pages8
DOIs
Publication statusPublished - 8 Feb 2018
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: 27 Nov 20171 Dec 2017

Conference

Conference2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
CountryUnited States
CityHonolulu
Period27/11/171/12/17

Keywords

  • pattern recognition
  • artificial neural networks
  • error estimation

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  • Cite this

    Tolo, S., Santhosh, T. V., Vinod, G., Oparaji, U., & Patelli, E. (2018). Uncertainty quantification methods for neural networks pattern recognition. In 2017 IEEE Symposium Series on Computational Intelligence https://doi.org/10.1109/SSCI.2017.8285163