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Proceedings Paper

Backward Euler-Maruyama method for a class of stochastic Markovian jump neural networks
Author(s): Hua Yang; Jianguo Liu; Yi Liu; Xiaofeng Yue
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Paper Abstract

Stability analysis of various neural networks have been successfully applied in many fields such as parallel computing and pattern recognition. This paper is concerned with a class of stochastic Markovian jump neural networks. The general mean-square stability of Backward Euler-Maruyama method for stochastic Markovian jump neural networks is discussed. The sufficient conditions to guarantee the general mean-square stability of Backward Euler-Maruyama method are given.

Paper Details

Date Published: 14 December 2015
PDF: 6 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 981319 (14 December 2015); doi: 10.1117/12.2230042
Show Author Affiliations
Hua Yang, Huazhong Univ. of Science and Technology (China)
Wuhan Polytechnic Univ. (China)
Jianguo Liu, Huazhong Univ. of Science and Technology (China)
Yi Liu, Huazhong Univ. of Science and Technology (China)
Xiaofeng Yue, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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