Identification Study of Sedimentary Environment Based on Fuzzy Neural Network

Zhu Yuan-xin; Liu Fu-chun

doi:10.3969/j.issn.1007-7162.2015.02.009

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Identification Study of Sedimentary Environment Based on Fuzzy Neural Network

更新时间：2026-05-27

- Identification Study of Sedimentary Environment Based on Fuzzy Neural Network
- Vol. 32, Issue 2, Pages: 48-52(2015)
- 作者机构：
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- DOI：10.3969/j.issn.1007-7162.2015.02.009
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- Published：2015
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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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