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Convergence analysis of hybrid bi-directional associative memory neural networks with discrete delays

机译:具有离散时滞的混合双向联想记忆神经网络的收敛性分析

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摘要

In this paper, the dynamical characteristics of hybrid bi-directional associative memory (BAM) neural networks with constant transmission delays are investigated. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the Lyapunov functionals are constructed and Halanay-type inequalities are respectively employed to derive the delay-independent sufficient conditions under which the networks converge exponentially to the equilibria associated with temporally uniform external inputs. Some examples are given to illustrate that the results are less conservative and less restrictive than the previously known results.
机译:本文研究了具有恒定传输时延的混合双向联想记忆(BAM)神经网络的动力学特性。在不假设突触连接权重对称,激活函数单调性和微分性的情况下,构造了Lyapunov函数,分别采用了Halanay型不等式来推导与延迟无关的充分条件,在该条件下网络以指数形式收敛到与时间相关的均衡统一的外部输入。给出了一些例子来说明结果比以前已知的结果更不保守也不那么严格。

著录项

  • 来源
    《Dynamical Systems》 |2003年第3期|p. 231-243|共13页
  • 作者单位

    Department of Computer Science and Engineering, Chongqing University, Chongqing 400044, P. R. China;

    College of Communications, Chongqing University, Chongqing 400044, P. R. China;

    Department of Computer Engineering and Information Technology, City University of Hong Kong, Hong Kong;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 工程基础科学;
  • 关键词

  • 入库时间 2022-08-17 13:08:33

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