>Bidirectional associative memory models are 2‐layer heteroassociative networks. In this work, we prove '/> Dynamics of BAM neural networks with mixed delays and leakage time‐varying delays in the weighted pseudo–almost periodic on time‐space scales
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首页> 外文期刊>Mathematical Methods in the Applied Sciences >Dynamics of BAM neural networks with mixed delays and leakage time‐varying delays in the weighted pseudo–almost periodic on time‐space scales
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Dynamics of BAM neural networks with mixed delays and leakage time‐varying delays in the weighted pseudo–almost periodic on time‐space scales

机译：CAM神经网络与混合延迟的动态和泄漏时变延迟在时空尺度上加权伪周期定期延迟

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摘要

>Bidirectional associative memory models are 2‐layer heteroassociative networks. In this work, we prove the existence and the global exponential stability of the unique weighted pseudo–almost periodic solution of bidirectional associative memory neural networks with mixed time‐varying delays and leakage time‐varying delays on time‐space scales. Some sufficient conditions are given for the existence, the convergence, and the global exponential stability of the weighted pseudo–almost periodic solution by using fixed‐point theorem and differential inequality techniques. The results of this paper complement the previous outcomes. An example is given to show the effectiveness of the derived results via computer simulations.
机译：

双向关联内存模型是2层异质化网络。 在这项工作中，我们证明了双向协会内存神经网络的唯一加权伪几乎周期性解决方案的存在和全局指数稳定性，具有混合时变延迟和泄漏时变延迟在时空尺度上。 通过使用定点定理和差分不等式技术，给出了存在的存在，收敛和全球指数稳定性的一些充分的条件。 本文的结果补充了以前的结果。 给出了通过计算机模拟显示派生结果的有效性。

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