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Abstract:
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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Source :
NEUROCOMPUTING
ISSN: 0925-2312
Year: 2015
Volume: 166
Page: 256-264
2 . 3 9 2
JCR@2015
5 . 7 1 9
JCR@2020
ESI Discipline: COMPUTER SCIENCE;
ESI HC Threshold:138
JCR Journal Grade:2
CAS Journal Grade:2
Cited Count:
WoS CC Cited Count: 14
SCOPUS Cited Count: 13
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 4
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