@inproceedings{f130651d1283478c8232502759ef8491,
title = "Empirical validation of the performance of a class of transient detector",
abstract = "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.",
keywords = "Acoustic Emission, Wavelet Transform, Wavelet Coefficient, Acoustic Emission Signal, Radial Basis Function Neural Network",
author = "Jacob, {Philip J.} and Ball, {Andrew D.}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1997.; AISB International Workshop on Evolutionary Computing 1997 ; Conference date: 07-04-1997 Through 08-04-1997",
year = "1997",
month = oct,
day = "1",
doi = "10.1007/bfb0027171",
language = "English",
isbn = "3540634762",
volume = "LNCS 1305",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "127--145",
editor = "David Corne and Shapiro, {Jonathan L.}",
booktitle = "Evolutionary Computing",
address = "Germany",
edition = "1st",
}