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<p>In the context of the significant data era, this study explores the application of fashion elements in ceramic design. By integrating modern aesthetics and traditional craftsmanship, it realizes the innovation of ceramic design and promotes the development of ceramic industry. A neural network-based assessment model, especially BP artificial neural network, is used to quantitatively analyze and apply fashion elements in ceramic design. First, the basic theory of neural network, including MP model and BP algorithm, is introduced. The study established a network model with a 5-30-5 structure by training 800 sets of color samples. The experimental results show that the network reaches the mean square error target (MSE=1e-7) after 1516 iterations, proving the model’s effectiveness in dealing with nonlinear color semantic mapping. The analysis of the prediction accuracy of the color scheme shows that the maximum errors are 2.22, 1.93, and 1.36 in the L, A, and B components, respectively. These results are lower than the national standard color difference range, indicating that the proposed model has practical application in ceramic design. The neural network model can effectively integrate fashion elements into ceramic design, providing a new perspective and technical support for traditional ceramic design.</p>
</abstract>

In the context of the significant data era, this study explores the application of fashion elements in ceramic design. By integrating modern aesthetics and traditional craftsmanship, it realizes the innovation of ceramic design and promotes the development of ceramic industry. A neural network-based assessment model, especially BP artificial neural network, is used to quantitatively analyze and apply fashion elements in ceramic design. First, the basic theory of neural network, including MP model and BP algorithm, is introduced. The study established a network model with a 5-30-5 structure by training 800 sets of color samples. The experimental results show that the network reaches the mean square error target (MSE=1e-7) after 1516 iterations, proving the model’s effectiveness in dealing with nonlinear color semantic mapping. The analysis of the prediction accuracy of the color scheme shows that the maximum errors are 2.22, 1.93, and 1.36 in the L, A, and B components, respectively. These results are lower than the national standard color difference range, indicating that the proposed model has practical application in ceramic design. The neural network model can effectively integrate fashion elements into ceramic design, providing a new perspective and technical support for traditional ceramic design.

Discussion on the use of fashion elements in ceramic design in the context of big data

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the context of the significant data era, this study explores the application of fashion elements in ceramic design. By integrating modern aesthetics and...

Discussion on the use of fashion elements in ceramic design in the context of big data

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 15 ene 2024

Aceptado: 20 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0606

Palabras clave
Big data, Fashion elements, Ceramic design, Neural network

© 2024 Jiabei Ye, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Discussion on the use of fashion elements in ceramic design in the context of big data

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 15 ene 2024

Aceptado: 20 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0606

Palabras claveBig data, Fashion elements, Ceramic design, Neural network

© 2024 Jiabei Ye, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Big data, Fashion elements, Ceramic design, Neural network