Empirical validation of the performance of a class of transient detector

Philip J. Jacob, Andrew D. Ball

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Transient detection in the presence of noise is a problem which occurs in many areas of engineering. A description is given of a classifier system suitable for the identification of high frequency waveforms. It uses the Wavelet Transform for signal pre-processing to produce a more parsimonious representation of the signal to be identified. A comparison is presented of the use of a Forward Selection algorithm and a Genetic Algorithm to pick appropriate indicator Variables as inputs to a classifier. A Radial Basis Function neural network is employed to model the class conditional probability density function. The classifier is applied to the identification of a number of high frequency Acoustic Emission signals, which are difficult to classify.

Original languageEnglish
Title of host publicationEvolutionary Computing
Subtitle of host publicationAISB International Workshop Manchester, UK, April 7–8, 1997 Selected Papers
EditorsDavid Corne, Jonathan L. Shapiro
Place of PublicationBerlin
PublisherSpringer Verlag
Number of pages19
VolumeLNCS 1305
ISBN (Electronic)9783540695783
ISBN (Print)3540634762, 9783540634768
Publication statusPublished - 1 Oct 1997
Externally publishedYes
EventAISB International Workshop on Evolutionary Computing 1997 - Manchester, United Kingdom
Duration: 7 Apr 19978 Apr 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceAISB International Workshop on Evolutionary Computing 1997
Country/TerritoryUnited Kingdom


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