14 December 2015 Backward Euler-Maruyama method for a class of stochastic Markovian jump neural networks
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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.
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Hua Yang, Hua Yang, Jianguo Liu, Jianguo Liu, Yi Liu, Yi Liu, Xiaofeng Yue, 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); doi: 10.1117/12.2230042; https://doi.org/10.1117/12.2230042
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