Hierarchical Learning/Perception and Conceptual Semantic Network System(HLPCS)

被引：0
|
作者：
Shim, JeongYon [1 ]
机构：
[1] Kangnam Univ, KNU Coll, Div GS, Comp Sci, Yongin, Gyeonggi Do, South Korea
来源：
2024 IEEE 19TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, ICIEA 2024 | 2024年
关键词：
memory modeling; conceptual semantic network; Learning; Perception; Description Knowledge Base; data extraction;
D O I：
10.1109/ICIEA61579.2024.10665289
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In the field of AI, research on building effective memory and management mechanisms for knowledge processing is very important. Memory framework effects on the performance of system directly. As one of these approaches, in this paper, research on memory modeling proposes a mechanism to construct memory by receiving information, learning and organizing it as personal experiences are accumulated. Hierarchical Learning/Perception and Conceptual Semantic Network System (HLPCS) was designed for efficient memory framework. In the experiment, the working performance of proposed system was simulated with data.
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页数：5
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