A novel similarity measurement for triangular cloud models based on dual consideration of shape and distance

Jianjun Yang; Jiahao Han; Qilin Wan; Shanshan Xing; Fei Chen

doi:10.7717/peerj-cs.1506

A novel similarity measurement for triangular cloud models based on dual consideration of shape and distance

PeerJ Comput Sci. 2023 Aug 9:9:e1506. doi: 10.7717/peerj-cs.1506. eCollection 2023.

Authors

Jianjun Yang^{1

2

3}, Jiahao Han¹, Qilin Wan¹, Shanshan Xing¹, Fei Chen^{1

2

3}

Affiliations

¹ School of Automobile and Transportation, Xihua University, Chengdu, Sichuan Province, China.
² Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan, Xihua University, Chendu, Sichuan Province, China.
³ Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chendu, Sichuan Province, China.

Abstract

It is important to be able to measure the similarity between two uncertain concepts for many real-life AI applications, such as image retrieval, collaborative filtering, risk assessment, and data clustering. Cloud models are important cognitive computing models that show promise in measuring the similarity of uncertain concepts. Here, we aim to address the shortcomings of existing cloud model similarity measurement algorithms, such as poor discrimination ability and unstable measurement results. We propose an EPTCM algorithm based on the triangular fuzzy number EW-type closeness and cloud drop variance, considering the shape and distance similarities of existing cloud models. The experimental results show that the EPTCM algorithm has good recognition and classification accuracy and is more accurate than the existing Likeness comparing method (LICM), overlap-based expectation curve (OECM), fuzzy distance-based similarity (FDCM) and multidimensional similarity cloud model (MSCM) methods. The experimental results also demonstrate that the EPTCM algorithm has successfully overcome the shortcomings of existing algorithms. In summary, the EPTCM method proposed here is effective and feasible to implement.

Keywords: Cloud model; Cloud model variance; EW-type closeness; Expectation curve; Similarity measurement; Triangular fuzzy number.

Grants and funding

This work was supported by the Open Research Fund of Sichuan Key Laboratory of Vehicle Measurement, Control and Safety (szjj2018-130); and the Sichuan Province Innovation Training Project (S202210623064 and S202210623048). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.