离散时间型复值神经网络的全局指数周期性OA北大核心CSCDCSTPCD
Global Exponential Periodicity of Discrete-Time Complex-Valued Neural Networks With Time-Delays
复值神经网络是神经网络的一个分支,也是最近几年快速发展的一个领域,在图像处理、模式识别、联想记忆等方面有广泛的应用.目前,对于复值神经网络动力学方面的研究主要集中在稳定性上,对于离散时间型复值神经网络周期性的研究还几乎没有.首先将连续时间型复值神经网络模型离散化得到离散时间型复值神经网络模型,然后利用M-矩阵理论、不等式技巧和Lyapu nov方法,获得了全局指数周期性的一个充分条件,最后给出的具有仿真的数值例子验证了获得结果的有效性.
Since the last decade,complex-valued neural networks have been rapidly developed and applied in various research areas,but few research has been done on the periodicity on discrete-time complex-valued neural networks.The periodicity of discrete-time complex-valued neural networks with time-delays was investigated.With the discretization technique,the discrete-time analogue of the continuous-time system with periodic input was formulated,and a sufficient cond…查看全部>>
胡进;宋乾坤
重庆交通大学理学院,重庆400074重庆交通大学理学院,重庆400074
数理科学
离散复值神经网络时滞全局指数周期性
discrete-time complex-valued neural networks time-delay global exponential periodicity
《应用数学和力学》 2013 (9)
时标上复数神经网络的单稳定性和多稳定性研究
929-940,12
The National Natural Science Foundation of China(61273021)国家自然科学基金资助项目(61273021)重庆市自然科学基金(重点)资助项目(cstc2013jjB40008)
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