An Improved Method for NURBS Surface Based on Particle Swarm Optimization BP Neural Network

• Main Authors: Xiaoqiang Tian, Lingfu Kong, Deming Kong, Li Yuan, Dehan Kong
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Reverse engineering; particle swarm optimization BP neural network; node vector; NURBS surface
• Online Access: https://ieeexplore.ieee.org/document/9217591/

In order to further improve the accuracy and speed of the present commonly used NURBS surface method, an improved method for NURBS surface based on particle swarm optimization BP neural network is proposed. Firstly, node vectors of the data points are calculated by using the parametrization method of accumulating chord length. Then, prediction model of node vectors is constructed by using the particle swarm optimization BP neural network, and the experiment is presented to justify the feasibility and veracity of constructed prediction model. Finally, using the predicted node vectors, a fast and high-precision NURBS surface is realized. The results showed that the root mean squared error of fitting result of surface was deduced 84.05% and the run time was deduced 92.42% compared with the traditional NURBS method. Therefore, the proposed method is a fast and high-precise NURBS surface fitting method.