基于D-S证据理论的猪舍环境状态识别研究

程捷; 冯天玉; 黄世明; 郝文彬; 霍俊; 朱望武

doi:10.13733/j.jcam.issn.2095-5553.2021.06.08

基于D-S证据理论的猪舍环境状态识别研究

1. 湖北工业大学;
2. 武汉市红之星农牧机械有限公司

基金项目:

国家重点研发计划项目(2018YFD0500700)

详细信息

作者简介:
程捷,男,1995年生,湖北麻城人,硕士研究生,研究方向为农业机械装备。E-mail:1808129470@qq.com

通讯作者:
冯天玉,男,1963年生,安徽南陵人,教授,硕导,研究方向为农业机械装备。E-mail:615971302@qq.com

中图分类号: S828.4
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出版历程
- 收稿日期: 2020-12-20
- 刊出日期: 2021-06-14

Research on recognition of piggery environmental state based on D-S evidence theory

1. Hubei University of Technology;
2. Wuhan Red Star Agro-Livestock Machinery Co.,Ltd.

摘要

摘要: 针对猪舍环境因子复杂且难以精确控制,提出一种基于D-S证据理论的数据融合算法对猪舍环境状态识别。首先,采集各个传感器的特征值,引入模糊隶属度函数确定概率分配函数;其次,采用改进K-L证据间距离,通过分配各个证据源融合权重来解决证据冲突问题;最后,将分配权重后的概率分配函数使用分布式融合机制得到最后识别结果。结果表明:D-S证据理论融合输出最高为0.629 3(状态Ⅲ),相比于下一项的0.119 8(状态Ⅱ)差值为0.509 5,识别效果显著,具有较高的实际应用价值。
- 猪舍环境 /
- D-S证据理论 /
- 隶属度函数 /
- 权重 /
- 状态识别
Abstract: Given the complexity and difficulty in accurately controlling the environmental factors of the pigsty, a data fusion algorithm based on D-S evidence theory was proposed for pigsty environmental state recognition. Firstly, the characteristic value of each sensor was collected, and the fuzzy membership function was introduced to determine the probability distribution function. Secondly, the improved K-L distance between pieces of evidence was used to solve the evidence conflict problem by allocating the fusion weight of each evidence source. Finally, the distributed fusion mechanism obtains the final recognition result for the probability assignment function after the weight assignment. The results show that the highest fusion output of D-S evidence theory is 0.629 3（state Ⅲ）, and the difference value is 0.509 5 compared with the next item, for which it is 0.119 8（state Ⅱ）. The recognition effect is significant and has high practical application value.
- piggery environment /
- D-S evidence theory /
- membership function /
- weight /
- status recognition

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参考文献(16)

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