Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313388170> ?p ?o ?g. }
Showing items 1 to 83 of
83
with 100 items per page.
- W4313388170 endingPage "1261" @default.
- W4313388170 startingPage "1246" @default.
- W4313388170 abstract "Abstract In today’s era of rapid development in science and technology, the development of digital technology has increasingly higher requirements for data processing functions. The matrix signal commonly used in engineering applications also puts forward higher requirements for processing speed. The eigenvalues of the matrix represent many characteristics of the matrix. Its mathematical meaning represents the expansion of the inherent vector, and its physical meaning represents the spectrum of vibration. The eigenvalue of a matrix is the focus of matrix theory. The problem of matrix eigenvalues is widely used in many research fields such as physics, chemistry, and biology. A neural network is a neuron model constructed by imitating biological neural networks. Since it was proposed, the application research of its typical models, such as recurrent neural networks and cellular neural networks, has become a new hot spot. With the emergence of deep neural network theory, scholars continue to combine deep neural networks to calculate matrix eigenvalues. This article aims to study the estimation and application of matrix eigenvalues based on deep neural networks. This article introduces the related methods of matrix eigenvalue estimation based on deep neural networks, and also designs experiments to compare the time of matrix eigenvalue estimation methods based on deep neural networks and traditional algorithms. It was found that under the serial algorithm, the algorithm based on the deep neural network reduced the calculation time by about 7% compared with the traditional algorithm, and under the parallel algorithm, the calculation time was reduced by about 17%. Experiments are also designed to calculate matrix eigenvalues with Obj and recurrent neural networks (RNNS) models, which proves that the Oja algorithm is only suitable for calculating the maximum eigenvalues of non-negative matrices, while RNNS is commonly used in general models." @default.
- W4313388170 created "2023-01-06" @default.
- W4313388170 creator A5081771675 @default.
- W4313388170 date "2022-01-01" @default.
- W4313388170 modified "2023-10-18" @default.
- W4313388170 title "Estimation and application of matrix eigenvalues based on deep neural network" @default.
- W4313388170 cites W2214007837 @default.
- W4313388170 cites W2360206994 @default.
- W4313388170 cites W2480932441 @default.
- W4313388170 cites W2513560036 @default.
- W4313388170 cites W2566971443 @default.
- W4313388170 cites W2608326745 @default.
- W4313388170 cites W2767844559 @default.
- W4313388170 cites W2788735937 @default.
- W4313388170 cites W2793685437 @default.
- W4313388170 cites W2938821438 @default.
- W4313388170 cites W2948302642 @default.
- W4313388170 cites W2949309915 @default.
- W4313388170 cites W2950455896 @default.
- W4313388170 cites W2962692363 @default.
- W4313388170 cites W2962864174 @default.
- W4313388170 cites W2964003203 @default.
- W4313388170 cites W2964198370 @default.
- W4313388170 cites W2989121801 @default.
- W4313388170 cites W2996728350 @default.
- W4313388170 cites W2999157820 @default.
- W4313388170 cites W3014440519 @default.
- W4313388170 cites W3099421421 @default.
- W4313388170 cites W3153871697 @default.
- W4313388170 doi "https://doi.org/10.1515/jisys-2022-0126" @default.
- W4313388170 hasPublicationYear "2022" @default.
- W4313388170 type Work @default.
- W4313388170 citedByCount "1" @default.
- W4313388170 countsByYear W43133881702023 @default.
- W4313388170 crossrefType "journal-article" @default.
- W4313388170 hasAuthorship W4313388170A5081771675 @default.
- W4313388170 hasBestOaLocation W43133881701 @default.
- W4313388170 hasConcept C106487976 @default.
- W4313388170 hasConcept C108583219 @default.
- W4313388170 hasConcept C11413529 @default.
- W4313388170 hasConcept C121332964 @default.
- W4313388170 hasConcept C154945302 @default.
- W4313388170 hasConcept C158693339 @default.
- W4313388170 hasConcept C159985019 @default.
- W4313388170 hasConcept C169756996 @default.
- W4313388170 hasConcept C192562407 @default.
- W4313388170 hasConcept C28940832 @default.
- W4313388170 hasConcept C41008148 @default.
- W4313388170 hasConcept C50644808 @default.
- W4313388170 hasConcept C62520636 @default.
- W4313388170 hasConceptScore W4313388170C106487976 @default.
- W4313388170 hasConceptScore W4313388170C108583219 @default.
- W4313388170 hasConceptScore W4313388170C11413529 @default.
- W4313388170 hasConceptScore W4313388170C121332964 @default.
- W4313388170 hasConceptScore W4313388170C154945302 @default.
- W4313388170 hasConceptScore W4313388170C158693339 @default.
- W4313388170 hasConceptScore W4313388170C159985019 @default.
- W4313388170 hasConceptScore W4313388170C169756996 @default.
- W4313388170 hasConceptScore W4313388170C192562407 @default.
- W4313388170 hasConceptScore W4313388170C28940832 @default.
- W4313388170 hasConceptScore W4313388170C41008148 @default.
- W4313388170 hasConceptScore W4313388170C50644808 @default.
- W4313388170 hasConceptScore W4313388170C62520636 @default.
- W4313388170 hasIssue "1" @default.
- W4313388170 hasLocation W43133881701 @default.
- W4313388170 hasOpenAccess W4313388170 @default.
- W4313388170 hasPrimaryLocation W43133881701 @default.
- W4313388170 hasRelatedWork W2002095939 @default.
- W4313388170 hasRelatedWork W2070382194 @default.
- W4313388170 hasRelatedWork W2104960301 @default.
- W4313388170 hasRelatedWork W2354828100 @default.
- W4313388170 hasRelatedWork W2356908423 @default.
- W4313388170 hasRelatedWork W2380025334 @default.
- W4313388170 hasRelatedWork W2470850800 @default.
- W4313388170 hasRelatedWork W2501514717 @default.
- W4313388170 hasRelatedWork W2510363947 @default.
- W4313388170 hasRelatedWork W856227370 @default.
- W4313388170 hasVolume "31" @default.
- W4313388170 isParatext "false" @default.
- W4313388170 isRetracted "false" @default.
- W4313388170 workType "article" @default.