Multimode system feature dimensionality reduction method

Abstract

The invention relates to a multimode system feature dimensionality reduction method. The method comprises the steps that M sets of sample feature vectors under different operation modes Fn of a multimode system are acquired; the sample feature vectors are successively normalized to acquire normalized sample feature vectors; a multimode sample normalized feature matrix is constructed; a local linear embedding algorithm is used to carry out nonlinear dimensionality reduction on the normalized feature matrix; the most similar N-1 dimensional features of the same mode are selected; a multidimensional scaling algorithm is used to carry out linear dimensionality reduction on the matrix in the step (4); and two-dimensional feature matrixes with the largest difference in modes of different classes are selected. According to the invention, feature extraction technologies of non-linear and linear manifold learning algorithms are integrated; through effective feature dimensionality reduction of high-dimensional data, the difficulty of multimode system mode recognition is reduced; linearity and nonlinearity structures of high-dimensional data are maximally preserved; and domain characteristics and distance similarity of high-dimensional data are maintained.

Description

technical field [0001] The invention relates to the technical field of feature dimension reduction of electronic equipment systems, in particular to a feature dimension reduction method of a multi-mode system. Background technique [0002] With the rapid development of the electronic industry and computer technology, the requirements for the systematic design and testing of electronic equipment are getting higher and higher, gradually developing from a single-mode system to the current mainstream multi-mode system. When system-level electronic devices work in different modes, the accompanying output signals are also complex and changeable, and it is often necessary to identify the mode environment in which the device is located from the signal characteristics of multiple modes. However, the feature dimension of the original signal collected from the system is relatively large, and it provides more information about objective phenomena. On the one hand, it brings great diffic...

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Problems solved by technology

However, the data after dimensionality reduction often cannot preserve the low-dimensional manifold structure of the data embedded in the high-dimensional space to the maximum extent, resulting in unsatisfactory feature discrimination of low-dimensional subspaces, which increases the difficulty of pattern recognition in multi-mode systems.

To solve such problems, it is necessary to study the feature extraction technology based on the manifold learning algorithm. At present, the fault feature extraction model based on a single nonlinear manifold learning algorithm greatly retains the overall geometric structure information in the fault signal, but does not consider The distance similarity between low-dimensional spatial features cannot handle the equidistant manifold problem of data well when the dimensionality reduction is large, resulting in a certain degree of discreteness in the distribution of two-dimensional features

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