[1]杨伟凯,王艳.知识推理框架下的改进自组织映射方法设计[J].智能系统学报,2023,18(5):926-935.[doi:10.11992/tis.202107013]
YANG Weikai,WANG Yan.A design of an improved self-organizing mapping method based on a knowledge reasoning framework[J].CAAI Transactions on Intelligent Systems,2023,18(5):926-935.[doi:10.11992/tis.202107013]
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YANG Weikai,WANG Yan.A design of an improved self-organizing mapping method based on a knowledge reasoning framework[J].CAAI Transactions on Intelligent Systems,2023,18(5):926-935.[doi:10.11992/tis.202107013]
知识推理框架下的改进自组织映射方法设计
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第5期
页码:
926-935
栏目:
学术论文—机器学习
出版日期:
2023-09-05
- Title:
- A design of an improved self-organizing mapping method based on a knowledge reasoning framework
- Keywords:
- knowledge reasoning; predicting; self-organized mapping; smart manufacturing; map matching; confidence; hyperbolic space; winning unit
- 分类号:
- TP274
- 摘要:
- 随着互联网技术的快速发展，在智能制造过程中会伴随着出现海量的工艺知识数据，为了提升对工艺数据的充分利用和掌握，提出一种知识推理框架下的改进自组织映射算法。在协同训练的思想下，对于知识库当中的工艺知识数据进行自组织映射网络下的筛选优胜，提高优胜单元的抗局部最优能力；利用改进自组织映射算法对特征优胜单元进行知识推理准则判断，在向量空间的映射下，通过双曲空间距离公式优选出置信度高的样本数据进行更新子代样本集；为了进一步提升特征信息的利用率，在知识推理框架下多次循环筛选提高工艺知识数据的有效预测。通过对铣削过程中真实数据进行建模仿真，验证了所提方法在面对多样本数据情况下的良好预测优化的性能。
- Abstract:
- In the rapidly developing world of Internet technologies, a vast amount of process knowledge data is generated in the smart manufacturing process. To effectively utilize this data and master it, we propose an improved self-organizing mapping algorithm within the framework of knowledge reasoning. The improved algorithm screens and optimizes the process knowledge data in the knowledge base using collaborative training, enhancing the anti-localization ability of the winning unit. The improved self-organizing mapping algorithm judges the knowledge reasoning criteria of the winning feature unit and selects sample data with high confidence through the mapping of vector space and the use of the hyperbolic space distance formula. Multiple cycles of screening are carried out to further improve the utilization of feature information and enhance the effective prediction of process knowledge data. Through the modeling and simulation of actual milling process data, the proposed method demonstrates its strong predictive and optimized performance when faced with multi-sample data.
- 参考文献/References:
-
[1] JIA Yanjie, CHEN Xi, YU Jieqiong, et al. Speaker recognition based on characteristic spectrograms and an improved self-organizing feature map neural network[J]. Complex & intelligent systems, 2021, 7(4): 1749-1757.
[2] QIU Dongwei, XU Hao, LUO Dean, et al. A rainwater control optimization design approach for airports based on a self-organizing feature map neural network model[J]. PLoS one, 2020, 15(1): e0227901.
[3] NG R W, BEGAM K M, RAJKUMAR R K, et al. An improved self-organizing incremental neural network model for short-term time-series load prediction[J]. Applied energy, 2021, 292: 116912.
[4] 王华鲜, 华容, 刘华平, 等. 无人机群多目标协同主动感知的自组织映射方法[J]. 智能系统学报, 2020, 15(3): 609-614
WANG Huaxian, HUA Rong, LIU Huaping, et al. Self-organizing feature map method for multi-target active perception of unmanned aerial vehicle systems[J]. CAAI transactions on intelligent systems, 2020, 15(3): 609-614
[5] 李昌华, 董鑫, 李智杰. 改进的半监督协同SOM图匹配算法[J]. 计算机工程与设计, 2019, 40(5): 1355-1359
LI Changhua, DONG Xin, LI Zhijie. Improved semi-supervised collaborative SOM graph matching algorithm[J]. Computer engineering and design, 2019, 40(5): 1355-1359
[6] UNDERWOOD K L, RIZZO D M, DEWOOLKAR M M, et al. Analysis of reach-scale sediment process domains in glacially-conditioned catchments using self-organizing maps[J]. Geomorphology, 2021, 382: 107684.
[7] SPASSIANI A C, MASON M S. Application of self-organizing maps to classify the meteorological origin of wind gusts in Australia[J]. Journal of wind engineering and industrial aerodynamics, 2021, 210: 104529.
[8] CHEN Yang, ASHIZAWA N, YEO C K, et al. Multi-scale self-organizing map assisted deep autoencoding Gaussian mixture model for unsupervised intrusion detection[J]. Knowledge-based systems, 2021, 224: 107086.
[9] JAYARATNE M, ALAHAKOON D, DE SILVA D. Unsupervised skill transfer learning for autonomous robots using distributed growing self organizing maps[J]. Robotics and autonomous systems, 2021, 144: 103835.
[10] DONG Bin, WENG Guirong, JIN Ri. Active contour model driven by self organizing maps for image segmentation[J]. Expert systems with applications, 2021, 177: 114948.
[11] 翟永杰, 杨旭, 赵振兵, 等. 融合共现推理的Faster R-CNN输电线路金具检测[J]. 智能系统学报, 2021, 16(2): 237-246
ZHAI Yongjie, YANG Xu, ZHAO Zhenbing, et al. Integrating co-occurrence reasoning for faster R-CNN transmission line fitting detection[J]. CAAI transactions on intelligent systems, 2021, 16(2): 237-246
[12] 饶子昀, 张毅, 刘俊涛, 等. 应用知识图谱的推荐方法与系统[J]. 自动化学报, 2021, 47(9): 2061-2077
RAO Ziyun, ZHANG Yi, LIU Juntao, et al. Recommendation methods and systems using knowledge graph[J]. Acta automatica sinica, 2021, 47(9): 2061-2077
[13] LIU Chenguang, YU Yongli, LI Xingxin, et al. Application of entity relation extraction method under CRF and syntax analysis tree in the construction of military equipment knowledge graph[J]. IEEE access, 2005, 8: 81-88.
[14] YAN Hehua, YANG Jun, WAN Jiafu. KnowIME: a system to construct a knowledge graph for intelligent manufacturing equipment[J]. IEEE access, 2020, 8: 41805-41813.
[15] YU Fang, ZHOU Qing, LU Xiaoshan, et al. A first-order logic framework of major choosing decision making with an uncertain reasoning function[J]. IEEE transactions on systems, man, and cybernetics:systems, 2018, 48(1): 89-98.
[16] XIONG Wenhan, HOANG T, WANG W Y. DeepPath: a reinforcement learning method for knowledge graph reasoning[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2017: 564?573.
[17] WANG Xiang, WANG Dingxian, XU Canran, et al. Explainable reasoning over knowledge graphs for recommendation[EB/OL]. (2018?11?12)[2020?01?01]. https://arxiv.org/abs/1811.04540.
[18] BELLOMARINI L, BENEDETTO D, GOTTLOB G, et al. Vadalog: a modern architecture for automated reasoning with large knowledge graphs[J]. Information systems, 2022, 105: 101528.
[19] ZHANG Rui, HRISTOVSKI D, SCHUTTE D, et al. Drug repurposing for COVID-19 via knowledge graph completion[J]. Journal of biomedical informatics, 2021, 115: 103696.
[20] WANG Qi, JI Yuede, HAO Yongsheng, et al. GRL: Knowledge graph completion with GAN-based reinforcement learning[J]. Knowledge-based systems, 2020, 209: 106421.
[21] 刘烨宸, 李华昱. 领域知识图谱研究综述[J]. 计算机系统应用, 2020, 29(6): 1-12
LIU Yechen, LI Huayu. Survey on domain knowledge graph research[J]. Computer systems & applications, 2020, 29(6): 1-12
[22] 杜会芳, 王昊奋, 史英慧, 等. 知识图谱多跳问答推理研究进展、挑战与展望[J]. 大数据, 2021, 7(3): 60-79
DU Huifang, WANG Haofen, SHI Yinghui, et al. Progress, challenges and research trends of reasoning in multi-hop knowledge graph based question answering[J]. Big data research, 2021, 7(3): 60-79
[23] MELIN P, MONICA J C, SANCHEZ D, et al. Analysis of spatial spread relationships of coronavirus (COVID-19) pandemic in the world using self organizing maps[J]. Chaos, solitons & fractals, 2020, 138: 109917.
[24] 王文广. 知识图谱推理: 现代的方法与应用[J]. 大数据, 2021, 7(3): 42-59
WANG Wenguang. Knowledge graph reasoning: modern methods and applications[J]. Big data research, 2021, 7(3): 42-59
[25] SHEN Fei, LANGARI R, YAN Ruqiang. Transfer between multiple machine plants: a modified fast self-organizing feature map and two-order selective ensemble based fault diagnosis strategy[J]. Measurement, 2020, 151: 107155.
[26] 许曈, 凌有铸, 陈孟元. 一种融合DGSOM神经网络的仿生算法研究[J]. 智能系统学报, 2017, 12(3): 405-412
XU Tong, LING Youzhu, CHEN Mengyuan. A bio-inspired algorithm integrated with DGSOM neural network[J]. CAAI transactions on intelligent systems, 2017, 12(3): 405-412
[27] XU Bo, ZHOU Feng, GATES A M. Multi-objective particle swarm optimization algorithm for the minimum constraint removal problem[J]. International journal of computational intelligence systems, 2020, 13(1): 291-299.
[28] 曹知奥, 汪晋宽, 韩英华, 等. 基于交叉-变异人工蜂群算法的微网优化调度[J]. 控制与决策, 2020, 35(9): 2059-2069
CAO Zhiao, WANG Jinkuan, HAN Yinghua, et al. Crossover-mutation based artificial bee colony algorithm for optimal scheduling of microgrid[J]. Control and decision, 2020, 35(9): 2059-2069
[29] AFOUDI Y, LAZAAR M, AL ACHHAB M. Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network[J]. Simulation modelling practice and theory, 2021, 113: 102375.
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备注/Memo
收稿日期:2021-7-12。
基金项目:国家重点研发计划项目（2018YFB1701903）.
作者简介:杨伟凯，硕士研究生，主要研究方向为工艺知识推理、机器学习、工艺能耗优化;王艳，教授，博士生导师，主要研究方向为离散制造能耗网络协同优化。参与国家高技术研究发展计划、重点研发计划等多项。授权发明专利16项，发表学术论文130余篇
通讯作者:王艳.E-mail：wangyan88@jiangnan.edu.cn
更新日期/Last Update:
1900-01-01