| 摘要: |
| 由高斯白噪声驱动的Ito型随机抛物型神经网络的稳定性，利用随机Lyapunov稳定性理论，Halanay不等式、改进的积分不等式，得到了与扩散项及时滞相关的稳定性判据，该条件在实际中容易验证，最后给出了数值算例，验证所得结果的有效性. |
| 关键词: It〖AKo^〗随机系统 神经网络 指数稳定 |
| DOI： |
| 分类号: |
| 基金项目:广州市属高校科技计划资助项目(08C018) |
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| Exponential stability of It〖AKo^〗 stochastic parabolic neural networks |
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| Abstract: |
| A class of Ito stochastic parabolic neural networks model was considered. The exponential stability condition of the systems was developed by using stability theory of stochastic system and improved integral inequality. The conditions were diffusion-dependent, which was clearly more accurate than the Poincare-type inequality in previously reported literatures. Finally, a numerical simulation example was provided to illustrate the feasibility and effective of the proposed method. |
| Key words: It〖AKo^〗stochastic systems neural networks exponential stability |
