Article: Intelligent classification of oil painting style based on dynamic fuzzy neural network Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.34 pp.82 - 99 Abstract: This article presents a dynamic fuzzy neural network (DFNN)-based intelligent classification approach for oil painting styles. When it comes to images like oil painting styles, which are high-dimensional, sophisticated and feature a lot of fuzzy elements, traditional oil painting style classification techniques still provide difficulties. DFNN teaches the deep features of oil painting images from data automatically by combining fuzzy logic and neural networks. Moreover, the dynamic learning mechanism of DFNN helps it to dynamically modify its structure and parameters in response to changes in the training data, hence preserving excellent classification accuracy in the face of new oil painting styles or style evolution. The testing results reveal that the technique greatly surpasses the conventional one in many respects, thereby offering fresh technical assistance for the automatic identification of oil paintings and other sectors. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent classification of oil painting style based on dynamic fuzzy neural network

Authors: Tianyi Xiao

Addresses: Art School, Wuhan University of Bioengineering, Wuhan, 430000, China

Abstract: This article presents a dynamic fuzzy neural network (DFNN)-based intelligent classification approach for oil painting styles. When it comes to images like oil painting styles, which are high-dimensional, sophisticated and feature a lot of fuzzy elements, traditional oil painting style classification techniques still provide difficulties. DFNN teaches the deep features of oil painting images from data automatically by combining fuzzy logic and neural networks. Moreover, the dynamic learning mechanism of DFNN helps it to dynamically modify its structure and parameters in response to changes in the training data, hence preserving excellent classification accuracy in the face of new oil painting styles or style evolution. The testing results reveal that the technique greatly surpasses the conventional one in many respects, thereby offering fresh technical assistance for the automatic identification of oil paintings and other sectors.

Keywords: oil painting style classification; dynamic fuzzy neural network; DFNN; intelligent classification; feature extraction; dynamic learning.

DOI: 10.1504/IJICT.2025.148820

International Journal of Information and Communication Technology, 2025 Vol.26 No.34, pp.82 - 99

Received: 20 May 2025
Accepted: 08 Jun 2025
Published online: 26 Sep 2025 *

Title: Intelligent classification of oil painting style based on dynamic fuzzy neural network

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