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A New Local Bipolar Autoassociative Memory Based on External Inputs of Discrete Recurrent Neural Networks With Time Delay

机译:基于时滞离散递归神经网络外部输入的新型局部双极自缔合记忆

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摘要

In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.
机译:在本文中,提出了基于具有一类增益类型激活函数的离散递归神经网络的局部双极自缔合记忆。神经网络的权重参数是通过一组不等式获得的,无需学习过程。建立全局指数稳定性标准是为了通过考虑时间延迟和外部输入来确保恢复模式的准确性。所提出的方法能够有效地克服虚假存储模式并实现存储容量。通过仿真实验验证了有效性,鲁棒性和容错能力。

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