Work

A dynamic cobweb resistance network solution based on a structured zeroing neural network and its applications

Year: 2025

Type: article

Abstract: Resistor networks are crucial in various fields, and solving problems on these is challenging. Existing numerical methods often suffer from limitations in accuracy and computational efficiency. In this paper, a structured zeroing neural network (SZNNCRN) for solving the mathematical model of time-varying cobweb resistance networks is proposed to address these challenges. Firstly, a SZNNCRN model is designed to solve the time-varying Laplacian equation system, which is a mathematical model representing the relationship between voltage and current in a cobweb resistance network. By leveraging the hidden structural attributes of a Laplacian matrix, the study devises optimized algorithms for the neural network models, which markedly improve computational efficiency. Subsequently, theoretical analyses validate the model's global exponential convergence, while numerical simulation results further corroborate its convergence and accuracy. Finally, the model is applied to calculate the equivalent resistance within a cobweb resistive network and for path planning on cobweb maps.

Source: Scientific Reports

DOI: 10.1038/s41598-025-89471-6

Authors: Xiaoyu Jiang, Dexin Meng, Yanpeng Zheng, Zhaolin Jiang

Institution: Linyi University

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funders: Department of Education of Shandong Province, National Natural Science Foundation of China, Natural Science Foundation of Shandong Province