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计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

基于模糊元胞自动机的网络舆情传播模型研究

党小超1,2,张春娇1,郝占军1,2   

  1. (1. 西北师范大学计算机科学与工程学院,兰州 730070;2. 甘肃省物联网工程研究中心,兰州 730070)
  • 收稿日期:2013-11-21 出版日期:2014-04-15 发布日期:2014-04-14
  • 作者简介:党小超(1963-),男,教授,主研方向:网络舆情传播;张春娇,硕士研究生;郝占军(通讯作者),讲师。
  • 基金资助:
    国家自然科学基金资助项目(61363059);甘肃省发展和改革委基金资助项目(010DKB021)。

Study on Propagation Model of Network Public Opinion Based on Fuzzy Cellular Automata

DANG Xiao-chao  1,2, ZHANG Chun-jiao 1, HAO Zhan-jun  1,2   

  1. Considering the fuzziness in the process of information dissemination, fuzzy algorithm is introduced into the classical model of Cellular Automata(CA) in this paper. By defining two fuzzy variables, social environment fitness and preference degree, the fuzzy CA model of network public opinion propagation process is set up. The evolution process of personal view in the forming process of public opinion is simulated and analyzed by fuzzy logic toolbox of Matlab. The result shows that the number of neutral group gradually increases, while those who hold extreme attitudes gradually decrease after extensive communication and discussion. They reach a compromise that this model can better describe the actual propagation of network public opinion.
  • Received:2013-11-21 Online:2014-04-15 Published:2014-04-14

摘要: 考虑到信息在传递过程中普遍存在模糊性的特点,结合经典元胞自动机理论和模糊推理算法,设计网络舆情传播的元胞自动机结构,定义环境适应度k和偏好度h 2个输入变量,建立网络舆情传播的模糊元胞自动机模型。对网络舆情传播中个体观点的演化过程进行Matlab仿真与分析,结果表明,在经过交流与讨论(对应于元胞进行足够多次数的迭代和演化)后,群体的观点和意见会出现归一的现象,归一不是归于处在2个极端的赞成或反对,而是向中间聚拢,最后形成一个折中的意见。该模型可以更好地描述网络舆情的实际传播过程。

关键词: 元胞自动机, 网络舆情传播模型, 模糊算法, Matlab仿真, 类聚, 归一化

Abstract: Considering the fuzziness in the process of information dissemination, fuzzy algorithm is introduced into the classical model of Cellular Automata(CA) in this paper. By defining two fuzzy variables, social environment fitness and preference degree, the fuzzy CA model of network public opinion propagation process is set up. The evolution process of personal view in the forming process of public opinion is simulated and analyzed by fuzzy logic toolbox of Matlab. The result shows that the number of neutral group gradually increases, while those who hold extreme attitudes gradually decrease after extensive communication and discussion. They reach a compromise that this model can better describe the actual propagation of network public opinion.

Key words: Cellular Automata(CA), propagation model of network public opinion, fuzzy algorithm, Matlab simulation, clustering, normalization

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