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Continuous attractors of higher-order recurrent neural networks with infinite neurons

机译:具有无限神经元的高阶递归神经网络的连续吸引子

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摘要

This paper investigates continuous attractors of higher-order recurrent neural networks (RNN) with infinite neurons (HRNNwIN). By employing the linearization technique, we present some new criteria on stable, semi-stable, positive semi-global stable and unstable continuous attractors. We also study the continuous attractors of this network under Lognormal distribution except Gaussian distribution. Finally, some simulations are finally carried out to further illustrate the developed theory.
机译:本文研究具有无限神经元(HRNNwIN)的高阶递归神经网络(RNN)的连续吸引子。通过使用线性化技术,我们提出了关于稳定,半稳定,正半全局稳定和不稳定连续吸引子的一些新标准。我们还研究了除高斯分布外在对数正态分布下该网络的连续吸引子。最后,最后进行一些模拟以进一步说明所发展的理论。

著录项

  • 来源
    《Neurocomputing》 |2014年第5期|388-396|共9页
  • 作者单位

    School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China;

    School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China;

    School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China;

    School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Continuous attractors; Recurrent neural networks; Higher-order interactions; Stable; Unstable; Semi-global stable;

    机译:连续吸引子;递归神经网络;高阶交互;稳定;不稳定半全局稳定;

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