Method for calculating feature value with largest sum of rear part and imaginary part of real matrix based on neural network

A technology of neural network and neural network model, which is applied in the field of computing real matrix real part imaginary part and the largest eigenvalue based on neural network, can solve the problem of not finding and paying attention to general real matrix neural network algorithm at the same time, and achieve the goal of improving accuracy Effect

Inactive Publication Date: 2018-09-18
CHENGDU NORMAL UNIV
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Problems solved by technology

However, recent papers have not found a neural network algorithm that simultaneously focuses on the sum of the imaginary and real parts of a general real matrix

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  • Method for calculating feature value with largest sum of rear part and imaginary part of real matrix based on neural network
  • Method for calculating feature value with largest sum of rear part and imaginary part of real matrix based on neural network
  • Method for calculating feature value with largest sum of rear part and imaginary part of real matrix based on neural network

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Embodiment Construction

[0039] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0040] refer to figure 1 , figure 1 Shown is a flow chart of a method for calculating the real imaginary part of a real matrix and the largest eigenvalue based on a neural network; as figure 1 As shown, the method 100 includes step 101 to step 105 .

[0041] In step 101, a complex neural network model is constructed to calculate the largest eigenvalue and eigenvector of the sum of the real and imaginary parts of the non-z...

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Abstract

The invention discloses a method for calculating a feature value with a largest sum of a rear part and an imaginary part of a real matrix based on a neural network. The method comprises: a complex neural network model for calculating a feature value with a largest sum of a rear part and an imaginary part of a non-zero real matrix and a feature vector is constructed; a preset iterative condition, an initial model parameter and an initial feature vector of the non-zero real matrix are obtained; according to the preset iterative condition, the initial model parameter and the initial feature vector, a feature vector xi of the non-zero real matrix is obtained by iterative calculation of the complex neural network model; when the feature vector xi is equal to zero, a model parameter u s adjustedaccording to a set increment and iterative calculation of the complex neural network model is carried out until the feature vector xi is not equal to zero; and when (lambda<R>m+lambdam)+u is larger than or equal to 0, a feature vector corresponding to the feature value of the largest sum of the rear part and the imaginary part is obtained; and according to the outputted feature vector xi, a feature value with the largest sum of the rear part and the imaginary part among feature values in the non-zero real matrix A is calculated.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to a method for calculating the real and imaginary parts of a real matrix and the largest eigenvalue based on a neural network. Background technique [0002] In the process of information processing and data retrieval in the prior art, in order to obtain the most accurate information data or retrieval data, most of the input information will be converted into a matrix during the processing process, and by obtaining the modulus of the matrix or all the eigenvalues ​​of the matrix The real part imaginary part and the largest eigenvalue can quickly find the information features that need to be accurately obtained. [0003] In the prior art, related research on obtaining matrix eigenvalues ​​and eigenvectors first appeared in the 1980s. OjaE proposed to use neural networks to calculate eigenvalues ​​and eigenvectors of symmetric matrices, which is mainly for the eigenvalu...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/02G06F17/16
Inventor 谭航万丽萍吴兆耀梁雪松龙国栋李少俊徐杨
Owner CHENGDU NORMAL UNIV
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