• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2010, Vol. 32 ›› Issue (3): 148-150.doi: 10.3969/j.issn.1007130X.2010.

• 论文 • 上一篇    下一篇

一种鲁棒性较强的神经元在模糊规则发生摄动时的有效控制

徐蔚鸿, 李璐伟   

  1. (长沙理工大学计算机与通信工程学院,湖南 长沙 410077)
  • 收稿日期:2009-06-13 修回日期:2009-10-25 出版日期:2010-03-10 发布日期:2010-03-10
  • 通讯作者: 徐蔚鸿 E-mail:younie@126.com
  • 作者简介:徐蔚鸿(1963-),男,教授,博士生导师,研究方向为智能系统、模式识别和软件工程等;李璐伟,硕士生,研究方向为模式识别、智能系统;

A Robust Neuron and Its Effective Control in the Perturbation of Fuzzy Rules

 XU Wei-Hong, LI Lu-Wei   

  1. (School of Computer and Communications,Changsha Unversity of Science and Technology,Changsha 410077,China)
  • Received:2009-06-13 Revised:2009-10-25 Online:2010-03-10 Published:2010-03-10
  • Contact: XU Wei-Hong E-mail:younie@126.com

摘要: 一种基于弱T范数和弱S范数的神经元,可以实现与、或和混合并模糊逻辑运算,并且拥有较强的鲁棒性。将它所组成的神经网络运用到模糊推理系统中,不仅可以简化网络,实现模糊推理最基本的一致性要求,还可以控制在模糊推理过程中当规则发生摄动时对推理结果的影响程度。

关键词: 神经元, 神经网络, 鲁棒性, 摄动, 控制

Abstract: The neurons based on a weak Tnorm and the weak Snorm can be achieved with and, or and mixedand fuzzy logic operations, and are robust. Based on the new neurons, a neural network is applied to fuzzy inference systems, which can not only simplify the network, satisfy the consistency principle of fuzzy inference, but also control the extent of the impact of the results of reasoning with the perturbation of rules in fuzzy inference.

Key words: neurons;neural network;robustness;perturbation of rules;control

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