期刊
NEUROCOMPUTING
卷 166, 期 -, 页码 256-264出版社
ELSEVIER
DOI: 10.1016/j.neucom.2015.03.072
关键词
Stochastic neural networks; Mean-square dissipativity; Numerical methods; Fractional Brownian motion; Poisson jumps
资金
- National High Technology Research and Development Program (863 Program) of China [2014AA041802]
- National Natural Science Foundation of China (NSFC) [61174095, 11261043, 61261044, 11461053]
In this paper, we introduce a class of stochastic neural networks with fractional Brownian motion (fBm) and Poisson jumps. We also concern mean-square dissipativity of numerical methods applied to a class of stochastic neural networks with fBm and jumps. The conditions under which the underlying systems are mean-square dissipative are considered. It is shown that the mean-square dissipativity is preserved by the compensated split-step backward Euler method and compensated backward Euler method without any restriction on stepsize, while the split-step backward Euler method and backward Euler method could reproduce mean-square dissipativity under a stepsize constraint. The results indicate that compensated numerical methods achieve superiority over non-compensated numerical methods in terms of mean-square dissipativity. Finally, an example is given for illustration. (C) 2015 Elsevier B.V. All rights reserved.
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