广东工业大学学报 ›› 2015, Vol. 32 ›› Issue (2): 48-52.doi: 10.3969/j.issn.1007-7162.2015.02.009

• 综合研究 • 上一篇    下一篇

基于模糊神经网络的沉积环境判别方法研究

朱远鑫,刘富春   

  1. 广东工业大学 计算机学院,广东 广州 510006
  • 收稿日期:2014-11-21 出版日期:2015-05-30 发布日期:2015-05-30
  • 作者简介:朱远鑫(1987-),男,硕士生,主要研究方向为模糊控制、数值计算. 刘富春(1971-),男,教授,主要研究方向为离散事件系统监控理论与应用、数理逻辑与模糊系统粗糙集理论与应用
  • 基金资助:

    国家自然科学基金资助项目(61273118)

Identification Study of Sedimentary Environment Based on Fuzzy Neural Network

Zhu Yuan-xin, Liu Fu-chun   

  1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2014-11-21 Online:2015-05-30 Published:2015-05-30

摘要: 由于粒度分析与沉积环境间密切的关系,针对模糊逻辑与人工神经网络各自的优点,提出了一种基于模糊神经网络的沉积环境判别方法.它以碎屑岩的关键粒度参数作为网络的输入,通过标准化和模糊化及输出的去模糊化等过程,使得模糊推理与神经网络充分结合.实验证明,这种模型判别相应沉积环境的误判率为9.1%,明显低于BP神经网络的32.1%且收敛速度更快,更能够满足实际工程的需求.

关键词: 模糊神经网络; 沉积环境; 判别分析; 粒度分析;

Abstract: As to the relationship between grain size and sedimentary environment, in this paper, an identification method of sedimentary environment is proposed based on fuzzy neural network, which combines the advantages of both fuzzy logic and artificial neural network. The proposed approach includes taking the key size parameters of clastic rock as inputs, being standardized and fuzzified by the network and being defuzzified of outputs. As a result, the fuzzy inference process is involved in the neural network sufficiently and successfully. The experiment shows that the improved network’s misjudgment rate of identification is 9.1%, less than 32.1% of BPNN obviously. Moreover, the former is faster than the latter in the aspect of convergence. Therefore, the network in this paper can fulfill the necessaries of practical projects.

Key words: fuzzy neural network; sedimentary environment; identification analysis; grain size analysis; classification

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