Method for extracting wavelet characteristic based on blur wavelet bag disintegrating

Abstract

The invention pertains to the signal treatment and pattern recognition technique. Particularly speaking, the invention discloses an extraction method for wavelet features based on fuzzy wavelet-packet decomposition by taking stationary signals or non-stationary signals as signal samples, comprising the training process to signal samples, the category of which is marked, and the extraction process to new signal samples, the category of which are unknown; the training process is taken as main treatment process through the training process to find optimum wavelet decomposition Omega<*>, and based on the optimum wavelet decomposition Omega<*> to extract wavelet coefficients features with high identification performance; in the feature extraction process of the unknown category samples, the located wavelet coefficients are extracted as final features. By adopting the invention to treat the stationary signals and non-stationary signals (including violent changing signal) and extract wavelet coefficient features with strong identification performance, the distance of signals within the same category is made as small as possible, while, the distance of signals of different categories is made as large as possible; thereby, the classification of stationary signals and non-stationary signals are finally achieved.

Description

technical field [0001] The invention relates to a signal classification technology for signal processing and pattern recognition, in particular to a wavelet feature extraction method based on fuzzy wavelet packet decomposition. Background technique [0002] In the field of signal processing and pattern recognition, signal feature extraction has always been the key point of research. Extracting good features can reduce the workload and achieve good classification results. At present, there are many kinds of mathematical transformations, such as: Fourier transform, K-L transform, discrete wavelet transform, wavelet packet (WP: WaveletPacket) decomposition, etc. The purpose of these transformations is to analyze the signal from another angle. Fourier transform analyzes signals in the frequency domain; K-L transform only considers the second-order energy characteristics of the signal; wavelet transform can extract strong discrimination from stationary or non-stationary signals (...

AI Technical Summary

Problems solved by technology

The following is an example to illustrate their shortcomings: there are two types of samples, one type obeys Gaussian distribution N(-10, 1), and the other type obeys Gaussian distribution N(10, 1), only considering the energy of the signal is It is impossible to distinguish these two types, but intuitively these two types of signals are easy to separate. From the perspective of signal classification, the above four wavelet packet decomposition methods based on signal energy are not optimal.

The method of combining the strong feature extraction ability of wavelet packet decomposition and the advantages of fuzzy theory in signal classification, and extracting wavelet coefficient features with strong classification ability from input stationary or non-stationary signals has not yet been reported.

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