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Existence and Global Attractability of Almost Periodic Solution for Competitive Neural Networks with Time-Varying Delays and Different Time Scales

机译:几乎定期解决竞争神经网络的存在和全局吸引力,具有时变延迟和不同的时间尺度

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The dynamics of cortical cognitive maps developed by self-organization must include the aspects of long and short-term memory. The behavior of such a neural network is characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural system, besides,this model bases on unsupervised synaptic learning algorithm. Considered the effect of time delays, we prove the existence, uniqueness and global attraction of the almost periodic solution by using fixed theorem and Variation-of-constants formula.
机译:由自组织开发的皮质认知地图的动态必须包括长期和短期内存的方面。这种神经网络的行为的特征在于神经活动的方程作为快速现象和突触修改的方程,作为神经系统的慢一部分,此外,这种模型基于无监督突触学习算法。考虑了时间延迟的效果,我们通过使用固定定理和常量变化公式来证明几乎定期解决的存在,独特性和全局吸引力。

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