Empirical validation of the performance of a class of transient detector
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,.
KeywordsAcoustic Emission Wavelet Transform Wavelet Coefficient Acoustic Emission Signal Radial Basis Function Neural Network
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