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author:

Huang, Lingmei (Huang, Lingmei.) [1] | Xia, Youshen (Xia, Youshen.) [2] (Scholars:夏又生) | Zhang, Songchuan (Zhang, Songchuan.) [3]

Indexed by:

EI Scopus

Abstract:

In recent years, matrix-valued optimization algorithms have been studied to enhance the computational performance of vector-valued optimization algorithms. This paper presents two matrix-type projection neural networks, continuous-time and discrete-time models, for solving matrix-valued optimization problems. The proposed continuous-time neural network may be viewed as a significant extension to the vector-type double projection neural network. More importantly, the proposed discrete-time projection neural network can be parallelly implemented in terms of matrix state space. Under pseudo-monotonicity condition and Lipschitz continuous condition, it is guaranteed that the two proposed matrix-type projection neural networks are globally convergent to the optimal solution. Finally, computed examples show that the two proposed matrix-type projection neural networks are much superior to the vector-type projection neural network in computation speed. © 2018, Springer Nature Switzerland AG.

Keyword:

Continuous time systems Matrix algebra Optimization

Community:

  • [ 1 ] [Huang, Lingmei]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
  • [ 2 ] [Xia, Youshen]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
  • [ 3 ] [Zhang, Songchuan]College of Mathematics and Computer Science, Wuyi University, Nanping, China

Reprint 's Address:

  • 夏又生

    [xia, youshen]college of mathematics and computer science, fuzhou university, fuzhou, china

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Source :

ISSN: 0302-9743

Year: 2018

Volume: 11302 LNCS

Page: 405-416

Language: English

0 . 4 0 2

JCR@2005

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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