Ceramic Art Image Design Based on Neural Network and Particle Image Algorithm

preprint OA: closed CC-BY-4.0
📄 Open PDF View at publisher

Abstract

In recent years, scientists have developed a new type of algorithm called convolutional neural network algorithm in the field of neural networks. This algorithm not only has a powerful image recognition function, but also can distinguish and arrange data images. At the same time, the algorithm's recognition and processing functions are also very powerful, able to identify relatively hidden images and process a very large image library in a short time. The research content of this article is the application and development of ceramic image creation based on the classification effect of neural network and the characteristics of quantum particle swarm algorithm. And according to the principles, standards, characteristics of neural network classification and the characteristics and technology of particle swarm algorithm, the traditional LB G algorithm and an improved LB G algorithm are discussed, and simulation experiments are carried out. During the experiment, the staff analyzed and optimized the specific process of the quantum particle swarm algorithm through a large number of calculations and simulation experiments. And according to the classification of neural network and quantum particle swarm algorithm, the researchers also proposed a set of practical ceramic image design methods. Through the inspection and comparison of the design results, the researchers preliminarily judged that the design method is not only practical, but also high Many advantages such as recognition, high accuracy and good visual experience. At the same time, the staff also optimized the method based on the preliminary design results.

My notes (saved in your browser only)

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. The paper's references may be in our DB but unresolved to ``paper_id`` (resolution happens at ingest when the cited DOI matches a row we already have). Run the cross-source citation reconcile pass to retry.

Source provenance

europepmc
last seen: 2026-05-19T01:45:01.086888+00:00
unpaywall
last seen: 2026-05-27T02:00:06.600101+00:00
License: CC-BY-4.0