Innovation of Enterprise Management Mode Based on Improved RBF Neural Network Algorithm

Zhang Jing

2025

Abstract

In a rapidly changing market environment, companies are facing increasing competitive pressure. In order to improve operational efficiency, optimize resource allocation and improve the quality of decision-making, this study explores the application of improved RBF neural network algorithm in enterprise management model innovation. Based on the comprehensive analysis of the internal management data of the enterprise, the management decision-making model and system based on RBF neural network are studied and constructed, and optimized and tested. The final experimental data show that the model integration system has significantly improved the prediction accuracy and response speed, and the comprehensive conclusion shows that the improved RBF neural network algorithm can effectively support the innovation of enterprise management mode and realize intelligent and refined management.

Paper Citation

in Harvard Style

Jing Z. (2025). Innovation of Enterprise Management Mode Based on Improved RBF Neural Network Algorithm. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 59-65. DOI: 10.5220/0013535500004664

in Bibtex Style

@conference{incoft25,
author={Zhang Jing},
title={Innovation of Enterprise Management Mode Based on Improved RBF Neural Network Algorithm},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={59-65},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013535500004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Innovation of Enterprise Management Mode Based on Improved RBF Neural Network Algorithm
SN - 978-989-758-763-4
AU - Jing Z.
PY - 2025
SP - 59
EP - 65
DO - 10.5220/0013535500004664
PB - SciTePress