广义分段常数时滞区间模糊 Cohen-Grossberg 神经网络的鲁棒稳定性分析
Robust stability analysis of interval fuzzy Cohen-Grossberg neural networks with piecewise constant argument of generalized type.
机构信息
Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
出版信息
Neural Netw. 2012 Sep;33:32-41. doi: 10.1016/j.neunet.2012.04.003. Epub 2012 Apr 24.
In this paper, existence and uniqueness of the solution of interval fuzzy Cohen-Grossberg neural networks with piecewise constant argument are discussed. Based on the comparison principle, it presents new theoretical results on the global robust exponential stability of interval fuzzy Cohen-Grossberg networks with piecewise constant argument. As a special case, the corresponding results of interval fuzzy recurrent neural networks with piecewise constant argument are derived. Three examples are given for illustrating validity of the obtained results.
本文讨论了具有分段常数变元的区间模糊 Cohen-Grossberg 神经网络解的存在唯一性。基于比较原理，给出了具有分段常数变元的区间模糊 Cohen-Grossberg 神经网络全局鲁棒指数稳定性的新理论结果。作为特例，导出了具有分段常数变元的区间模糊递归神经网络的相应结果。给出了三个实例来说明所得结果的有效性。
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