6533b838fe1ef96bd12a3b04

RESEARCH PRODUCT

Prefiltering for pattern recognition using wavelet transform and neural networks

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Publisher Summary Neural networks are built from simple units interlinked by a set of weighted connections. Generally, these units are organized in layers. Each unit of the first layer (input layer) corresponds to a feature of a pattern that is to be analyzed. The units of the last layer (output layer) produce a decision after the propagation of information. Before feeding the computational data to neural networks, the signal must undergo a preprocessing in order to (1) define the initial transformation to represent the measured signal, (2) retain important features for class discrimination and discard that is irrelevant, and (3) reduce the volume of data to be processed, for example, data compression. This stage of preprocessing can be realized using many techniques: principal component analysis (PCA), Fourier transform, and other algorithms that allow the selection of the best parameters. This chapter focuses on pattern recognition using wavelet transform and neural networks.