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

doi:10.3969/j.issn.1007-7162.2015.02.009

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基于模糊神经网络的沉积环境判别方法研究

综合研究 | 更新时间：2026-05-27

- 基于模糊神经网络的沉积环境判别方法研究
- Identification Study of Sedimentary Environment Based on Fuzzy Neural Network
- 广东工业大学学报 2015年32卷第2期页码：48-52
- 作者机构：
- 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
- 基金信息：
  
  国家自然科学基金资助项目（61273118）
- DOI：10.3969/j.issn.1007-7162.2015.02.009
  中图分类号：
- 纸质出版：2015
- 稿件说明：
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朱远鑫, 刘富春. 基于模糊神经网络的沉积环境判别方法研究[J]. 广东工业大学学报, 2015, 32(2): 48-52.

Zhu Yuan-xin, Liu Fu-chun. Identification Study of Sedimentary Environment Based on Fuzzy Neural Network[J]. 2015, 32(2): 48-52.
朱远鑫, 刘富春. 基于模糊神经网络的沉积环境判别方法研究[J]. 广东工业大学学报, 2015, 32(2): 48-52. DOI： 10.3969/j.issn.1007-7162.2015.02.009.

Zhu Yuan-xin, Liu Fu-chun. Identification Study of Sedimentary Environment Based on Fuzzy Neural Network[J]. 2015, 32(2): 48-52. DOI： 10.3969/j.issn.1007-7162.2015.02.009.

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.

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.

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