首页 > 过刊浏览>2023年第42卷第3期 >88-96. DOI:10.13300/j.cnki.hnlkxb.2023.03.011
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基于三角模糊数层次分析法的海参养殖水质评价
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作者:
作者单位:

1.大连海洋大学信息工程学院， 大连 116023;2.大连鑫玉龙海洋生物种业科技股份有限公司，大连 116000

作者简介:

张淑雨，E-mail：3112989618@qq.com

通讯作者:

王魏，E-mail：ww_wangwei@dlou.edu.cn

中图分类号:

S959

基金项目:

设施渔业教育部重点实验室 （大连海洋大学）开放课题（202314）；辽宁省教育厅青年科技人才“育苗”项目（QL201912）


Water quality evaluation of sea cucumber culture based on triangular fuzzy number analytic hierarchy process
Author:
Affiliation:

1.School of Information Engineering, Dalian Ocean University, Dalian 116023, China;2.Dalian Xinyulong Marine Biological Seed Technology Co., LTD.,Dalian 116000,China

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    摘要:

    为了解和掌握海参养殖过程水质状况，对海参养殖水质使用融合三角模糊数层次分析法的多级模糊评价方法评估。首先依据水质关键因子在海参生长过程中所起积极和消极作用将水质关键因子分类，并依据2类关键因子设计“海参养殖水质正、负相关因子模糊推理系统”。其次，将两模糊推理系统作为一级模糊，其结果作为二级模糊的输入。二级模糊系统通过一级正、负相关因子推理结果得到最终水质评价结果。在模糊推理过程中，采用三角模糊数层次分析法为海参水质关键因子赋权来提高评价结果的准确性。最后，将该方法与融合三角模糊数层次分析法的海参养殖水质单级模糊评价系统、海参养殖水质ANFIS模糊评价进行对比试验。结果显示，3种方法评价结果一致，融合三角模糊数层次分析法的海参养殖水质多级模糊评价法将模糊规则由原来的243条减少为45条，缓解了维数灾难问题；且与ANFIS模糊评价相比无需经过大量调参和训练，占用系统资源更少。以上结果表明，融合三角模糊数层次分析法更适用于海参养殖中的水质评价与管理。

    Abstract:

    In order to understand and master the water quality status of sea cucumber aquaculture， a multi-level fuzzy evaluation method using a fusion of triangular fuzzy number and analytic hierarchy process was used to evaluate the water quality of sea cucumber farming. Firstly， the water quality key factors were classified based on their positive and negative effects on sea cucumber growth during the farming process， and a “positive/negative correlation factor fuzzy reasoning system for sea cucumber aquaculture water quality” was designed based on the two categories of key factors. Secondly， the two fuzzy reasoning systems were used as inputs for the first level fuzzy system， and the results were used as inputs for the second level fuzzy system. The final water quality evaluation results were obtained through the second level fuzzy system based on the first level positive/negative correlation factor reasoning results. During the fuzzy reasoning process， the triangular fuzzy number analytic hierarchy process was used to assign weights to the sea cucumber water quality key factors in order to improve the accuracy of the evaluation results. Finally， this method was compared to the single-level fuzzy evaluation system and ANFIS fuzzy evaluation system using a fusion of triangular fuzzy number and analytic hierarchy process for evaluating the water quality of sea cucumber aquaculture. The results showed that the three methods produced consistent evaluation results. The multi-level fuzzy evaluation method for sea cucumber aquaculture water quality using a fusion of triangular fuzzy number and analytic hierarchy process reduced the number of fuzzy rules from the original 243 to 45， alleviating the problem of dimensionality catastrophe. Moreover， it requires less parameter tuning and training compared to the ANFIS fuzzy evaluation system and occupies fewer system resources. These results indicate that the fusion of triangular fuzzy number and analytic hierarchy process is more suitable for water quality evaluation and management in sea cucumber aquaculture.

    表 1 海参水质因子判断矩阵Table 1 Sea cucumber water quality factor judgment matrix
    表 2 输入模糊集论域划分Table 2 Domain division table of input fuzzy set theory
    表 3 3种评价方法试验样本数据Table 3 Experimental sample data table of three evaluation methods
    图1 融合三角模糊数层次分析法的海参养殖水质多级模糊系统结构图Fig.1 Structure diagram of multilevel fuzzy system of aquacultural water quality of sea cucumber with triangular fuzzy number analytic hierarchy process
    图2 一级模糊输入温度（A）及亚硝酸盐（B）隶属度函数图Fig.2 First order fuzzy input temperature（A） and nitrite（B） membership function
    图3 二级模糊输入隶属度函数图Fig.3 Membership function diagram of secondary fuzzy input
    图4 一级输出隶属度函数图Fig.4 Membership function diagram of first-level output
    图5 二级输出隶属度函数图Fig.5 Membership function diagram of secondary output
    图6 多级模糊系统结构框架Fig.6 Multi-stage fuzzy system structure of sea cucumber aquaculture water quality with entropy weight
    图7 一级正、负相关模糊系统结构Fig.7 Structure diagram of positive and negative correlation fuzzy system
    图8 二级模糊系统结构图Fig.8 Structure diagram of secondary fuzzy system
    图9 融合三角模糊数层次分析法的海参养殖水质单级模糊评价结构Fig.9 Structure diagram of single level fuzzy evaluation of aquacultural water quality of sea cucumber with triangular fuzzy number analytic hierarchy process
    图10 ANFIS结构Fig.10 ANFIS structure diagram
    表 4 3种评价方法评价结果Table 4 Comparison of evaluation results of the three evaluation methods
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张淑雨,王魏,刘懿纬,孙俊洋,李双双.基于三角模糊数层次分析法的海参养殖水质评价[J].华中农业大学学报,2023,42(3):88-96

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  • 收稿日期:2022-09-30
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