Backward Euler-Maruyama method for a class of stochastic Markovian jump neural networks

Hua Yang; Jianguo Liu; Yi Liu; Xiaofeng Yue

doi:10.1117/12.2230042

14 December 2015 Backward Euler-Maruyama method for a class of stochastic Markovian jump neural networks

Hua Yang, Jianguo Liu, Yi Liu, Xiaofeng Yue

Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 981319 (2015) https://doi.org/10.1117/12.2230042
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

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.

Citation Download Citation

Hua Yang, Jianguo Liu, Yi Liu, and Xiaofeng Yue "Backward Euler-Maruyama method for a class of stochastic Markovian jump neural networks", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 981319 (14 December 2015); https://doi.org/10.1117/12.2230042

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available