Research on standard quality evaluation method based on fuzzy neural network

Ting Xu; Junfeng Chen; Hua Zhang; Ru Li; Yixin Qu

doi:10.1117/12.2637528

24 May 2022 Research on standard quality evaluation method based on fuzzy neural network

Ting Xu, Junfeng Chen, Hua Zhang, Ru Li, Yixin Qu

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Proceedings Volume 12260, International Conference on Computer Application and Information Security (ICCAIS 2021); 122601E (2022) https://doi.org/10.1117/12.2637528
Event: International Conference on Computer Application and Information Security (ICCAIS 2021), 2021, Wuhan, China

Abstract

In view of the characteristics of standard quality evaluation index diversification, multi-dimension and the result susceptible to subjective factors, a standard quality evaluation model based on T-S model of fuzzy neural network is established by introducing fuzzy system theory. This model combines the advantages of fuzzy system and neural network, such as good fuzzy knowledge expression ability and adaptive ability. It has the advantages of optimal result approximation, short training time and fast convergence speed. This model was used to evaluate the quality of 7 standards in the field of petroleum engineering. The results show that the fuzzy neural network method could solve the fuzzy data processing problem in standard quality evaluation, and the reliability of the evaluation model could also meet the requirements of standard quality evaluation.

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Ting Xu, Junfeng Chen, Hua Zhang, Ru Li, and Yixin Qu "Research on standard quality evaluation method based on fuzzy neural network", Proc. SPIE 12260, International Conference on Computer Application and Information Security (ICCAIS 2021), 122601E (24 May 2022); https://doi.org/10.1117/12.2637528

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KEYWORDS

Fuzzy logic

Standards development

Neural networks

Reliability

Quality systems

Error analysis

Fuzzy systems

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