convergence and stability of the split-step θ-milstein method for stochastic delay hopfield neural networks
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ID: 216484
2013
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Abstract
A new splitting method designed for the numerical solutions of stochastic delay Hopfield neural networks is introduced and analysed. Under Lipschitz and linear growth conditions, this split-step θ-Milstein method is proved to have a strong convergence of order 1 in mean-square sense, which is higher than that of existing split-step θ-method. Further, mean-square stability of the proposed method is investigated. Numerical experiments and comparisons with existing methods illustrate the computational efficiency of our method.
| Reference Key |
guo2013abstractconvergence
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|---|---|
| Authors | ;Qian Guo;Wenwen Xie;Taketomo Mitsui |
| Journal | science and technology of advanced materials |
| Year | 2013 |
| DOI |
10.1155/2013/169214
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| URL | |
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