[1]郭文强,高晓光,侯勇严,等.采用MSBN多智能体协同推理的智能农业车辆环境识别[J].智能系统学报,2013,8(5):453-458.[doi:10.3969/j.issn.1673-4785.201210057]
GUO Wenqiang,GAO Xiaoguang,HOU Yongyan,et al.Environment recognition of intelligent agricultural vehicles based on MSBN and multi-agent coordinative inference[J].CAAI Transactions on Intelligent Systems,2013,8(5):453-458.[doi:10.3969/j.issn.1673-4785.201210057]
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GUO Wenqiang,GAO Xiaoguang,HOU Yongyan,et al.Environment recognition of intelligent agricultural vehicles based on MSBN and multi-agent coordinative inference[J].CAAI Transactions on Intelligent Systems,2013,8(5):453-458.[doi:10.3969/j.issn.1673-4785.201210057]
采用MSBN多智能体协同推理的智能农业车辆环境识别
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
8
期数:
2013年第5期
页码:
453-458
栏目:
学术论文—机器感知与模式识别
出版日期:
2013-10-25
- Title:
- Environment recognition of intelligent agricultural vehicles based on MSBN and multi-agent coordinative inference
- 文章编号:
- 1673-4785(2013)05-0453-06
- Keywords:
- intelligent agricultural vehicle; multiply sectioned Bayesian network (MSBN); multi-agent; coordinative inference; environment recognition
- 分类号:
- TP391
- 文献标志码:
- A
- 摘要:
- 为了解决智能农业车辆对所处复杂农田环境的识别信度定量分析困难的问题，提出了基于多连片贝叶斯网(MSBN)多智能体协同推理的目标识别算法.该方法把多智能体图像采集系统的局部信息表征在MSBN模型中，在观测不完备条件下，虽然单个智能体仅拥有目标的局部观测信息，但利用重叠子域信息的更新可以进行子网间消息的传播.利用MSBN局部推理和子网间信度通信的全局推理对多源信息进行融合，以提高识别性能.实验结果表明，与传统神经网络或BN方法相比，基于MSBN目标识别算法有效地对多源信息进行了补充，可以提高农业车辆在复杂环境进行识别的准确性.
- Abstract:
- In order to solve the problem existing in the agricultural environment recognition of intelligent vehicles, due to the difficulty of conducting quantitative analysis of the reliability of such recognition, a target recognition algorithm for multi-agent cooperative inference based on the multiply sectioned Bayesian network (MSBN) has been proposed. This method characterizes local information of the multi-agent image acquiring system with MSBN model. In the circumstance of incomplete observations, although each single agent may only capture some local observation information from the target, the message propagation among subnets can be achieved by information update in the overlapping subdomains. By combining the local inference and global inference of reliability communication between subnets in MSBN, the multi-source information was merged to enhance recognition performance. By comparing the traditional neural network and BN method, experimental results illustrate that, the target recognition algorithm based on MSBN can effectively supplement multi-source information, and thus, can improve the recognition accuracy of agricultural vehicles in the complicated environment.
- 参考文献/References:
-
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备注/Memo
收稿日期：2012-10-27. 网络出版日期：2013-05-15.
基金项目：国家自然科学基金资助项目(90205019, 60774064)；陕西科技大学博士科研启动基金资助项目(BJ12-03)；陕西省教育厅科研计划资助项目(2013JK1114).
通信作者：郭文强. E-mail: guoweiqiang@sust.edu.cn.
作者简介：
郭文强，男，1971年生，副教授，硕士生导师，主要研究方向为电子信息、智能系统.参与国家自然科学基金项目2项，发表学术论文18篇，其中被EI、ISTP检索11篇.
高晓光，女，1957年生，教授，博士生导师，中国宇航学会光电技术专业委员会委员，享受国务院政府特殊津贴，主要研究方向为先进控制理论及其在复杂系统中的应用.承担国家211项目、国家自然科学基金、国家“973”计划、国防科技预研项目、国家教委基金、国防预研基金、航空科学基金及国家重点型号工程相关研究项目等30余项.发表学术论文100余篇，其中被EI、ISTP检索40余篇.
侯勇严，女，1972年生，副教授，主要研究方向为智能控制.参与多项省部级科研项目，发表学术论文12篇，其中被EI、ISTP检索6篇.
更新日期/Last Update:
2013-11-28