Cognitive excursion analysis of uncertainty concepts based on cloud model

IF 1.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xu C. Lin
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引用次数: 0

Abstract

Based on the characteristics of human cognition and the cloud model, the excursion of uncertain concepts by the similarity between uncertain concepts from the perspective of conceptual cognition is studied. Firstly, considering the different meanings of the numerical characters in cloud concept, the properties of cloud concepts similarity are given. Furthermore, in order to reflect the various similarities that may exist in uncertain concept, five similarities between cloud concepts are constructed, specifically include the similarity between concept expectations, the similarity between concept entropy, the similarity between concept hyper entropy, the shape similarity and the overall similarity. Secondly, the rationality of these proposed similarity measurements is illustrated by comparing with the existing methods through the specific data analysis. Finally, two cognitive experiments, including concept cognitive processes without prior knowledge and concept cognitive processes with prior knowledge (including positive prior knowledge and negative prior knowledge), are designed. These experiments are all used to study the excursion in the process of concept cognition by the similarity of cloud concepts. The experiment results show that the proposed similarities are reasonable by comparing the proposed method with the existing ones, and the effectiveness of the proposed method is also verified by the excursion of concept cognition.

Abstract Image

基于云模型的不确定性概念认知偏移分析
基于人类认知和云模型的特点,从概念认知的角度研究了利用不确定概念之间的相似性对不确定概念的偏移。首先,考虑云概念中数字字符的不同含义,给出了云概念相似度的性质;进一步,为了反映不确定概念中可能存在的各种相似度,构建了云概念间的五种相似度,具体包括概念期望之间的相似度、概念熵之间的相似度、概念超熵之间的相似度、形状相似度和整体相似度。其次,通过具体的数据分析,通过与现有方法的比较,说明所提出的相似性度量方法的合理性。最后,设计了两个认知实验,包括无先验知识的概念认知过程和有先验知识(包括积极先验知识和消极先验知识)的概念认知过程。这些实验都是为了研究云概念相似性在概念认知过程中的偏移。实验结果表明,所提方法与现有方法的相似性是合理的,并通过概念认知的偏移验证了所提方法的有效性。
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来源期刊
Cognitive Computation and Systems
Cognitive Computation and Systems Computer Science-Computer Science Applications
CiteScore
2.50
自引率
0.00%
发文量
39
审稿时长
10 weeks
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