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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Multistability of Almost Periodic Solution for Memristive Cohen-Grossberg Neural Networks With Mixed Delays
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Multistability of Almost Periodic Solution for Memristive Cohen-Grossberg Neural Networks With Mixed Delays

机译：椎间盘议记忆COHEN-GROSSBERG神经网络与混合延迟的多态性

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摘要

This paper presents the multistability analysis of almost periodic state solutions for memristive Cohen-Grossberg neural networks (MCGNNs) with both distributed delay and discrete delay. The activation function of the considered MCGNNs is generalized to be nonmonotonic and nonpiecewise linear. It is shown that the MCGNNs with n-neuron have ( K + 1)(n) locally exponentially stable almost periodic solutions, where nature number K depends on the geometrical structure of the considered activation function. Compared with the previous related works, the number of almost periodic state solutions of the MCGNNs is extensively increased. The obtained conclusions in this paper are also capable of studying the multistability of equilibrium points or periodic solutions of the MCGNNs. Moreover, the enlarged attraction basins of attractors are estimated based on original partition. Some comparisons and convincing numerical examples are provided to substantiate the superiority and efficiency of obtained results.
机译：本文介绍了椎间盘科恩 - 格尔伯格神经网络（MCGNNS）几乎定期的状态解决方案的多态性分析，分布式延迟和离散延迟。所考虑的MCGNN的激活函数是广泛的，是非单调和非实用性线性。结果表明，具有n-neuron的MCGNN具有（k + 1）（n）局部指数稳定的几乎周期性解决方案，其中自然数k取决于所考虑的激活功能的几何结构。与以前的相关工作相比，MCGNNS的几乎定期状态解决方案的数量广泛增加。本文所获得的结论还能够研究MCGNNS的平衡点或周期性溶液的多态性。此外，基于原始分区估计吸引子的扩大吸引力盆地。提供了一些比较和说服数值示例以证实得到的结果的优越性和效率。

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