Research on Optimization of Enterprise Financial Performance Evaluation System Based on RBF Neural Network Algorithm in the Era of Data Analysis
Main Article Content
Abstract
The increasing complexity of multi-source heterogeneous financial data requires intelligent nonlinear modeling approaches capable of efficient feature extraction and adaptive decision-making. To address the limitations of conventional financial performance evaluation systems, this study proposes an optimized enterprise financial performance evaluation framework based on a Radial Basis Function (RBF) neural network. A multidimensional indicator system covering profitability, solvency, operational capability, growth potential, cash flow, and financial risk control is first reconstructed, and factor analysis is employed to eliminate redundant variables and generate compact feature representations for network training. A three-layer RBF neural network is subsequently established to perform nonlinear mapping between financial indicators and comprehensive performance scores, enabling objective evaluation without subjective weight assignment. Empirical validation using enterprises from multiple industries demonstrates that the proposed model achieves superior evaluation accuracy, faster convergence, enhanced generalization capability, and improved dynamic prediction performance compared with traditional assessment approaches. The integration of factor analysis and RBF learning effectively captures latent nonlinear relationships embedded in complex financial data and provides a scalable intelligent evaluation architecture. Beyond financial management, the proposed feature extraction and nonlinear inference strategy offers valuable methodological references for multidimensional signal analysis, adaptive pattern recognition, electromagnetic information processing, and antenna-assisted intelligent sensing systems that require robust modeling of heterogeneous data streams.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
J. Ye, “Application of improved RBF neural network algorithm in hierarchical management of enterprise,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, 2024, doi: 10.2478/AMNS-2024-0741.
C. Zheng, L. Xiaoqi, G. Jie, et al., “Sports injury early warning of basketball players based on RBF neural network algorithm,” Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4291-4300, 2023, doi: 10.3233/JIFS-224601.
W. Kaiyan and Z. Qinghua, “GSM-R wireless field strength coverage prediction algorithm based on PSO-RBF neural network algorithm,” Journal of Physics: Conference Series, vol. 2383, no. 1, 2022, doi: 10.1088/1742-6596/2383/1/012097.
P. Chunliang and P. Yuning, “The Promotion Effect of Computer-Aided Technology Combined with RBF Neural Network Algorithm on Art Design,” Advances in Multimedia, vol. 2022, 2022, doi: 10.1155/2022/7511630.
M. Cui and Y. Fu, “Analysis of Forest Carbon Sequestration Management Plan Based on Neural Network Algorithm,” Agricultural & Forestry Economics and Management, vol. 5, no. 1, 2022, doi: 10.23977/AGRFEM.2022.050101.
X. Chengqi, “Early Warning Model of Track and Field Sports Injury Based on RBF Neural Network Algorithm,” Journal of Physics: Conference Series, vol. 2037, no. 1, 2021, doi: 10.1088/1742-6596/2037/1/012084.
H. Fuxing, “Early Warning Model of Sports Injury Based on RBF Neural Network Algorithm,” COMPLEXITY, vol. 2021, 2021, doi: 10.1155/2021/6622367.
B. Li, “The Role of Improved Neural Network Algorithm in Convertible Bond Market Analysis,” Academic Journal of Business & Management, vol. 1, no. 3, 2019.
“Energy - Solar Energy; Study Data from University of Tunis Update Understanding of Solar Energy (Real-time Study of a Photovoltaic System With Boost Converter Using the Pso-rbf Neural Network Algorithms In a Myrio Controller),” Network Weekly News, 2019.
L. Long, “Research on four axis manipulator trajectory tracking Based on RBF Neural Network Algorithm,” Journal of Physics: Conference Series, vol. 1187, no. 3, 2019, doi: 10.1088/1742-6596/1187/3/032016.
“Algorithms; Data from China University of Mining and Technology Provide New Insights into Algorithms (An optimized RBF neural network algorithm based on partial least squares and genetic algorithm for classification of small sample),” Computers, Networks & Communications, 2017.
W. Jia, D. Zhao, and L. Ding, “An optimized RBF neural network algorithm based on partial least squares and genetic algorithm for classification of small sample,” Applied Soft Computing, vol. 48, pp. 373-384, 2016, doi: 10.1016/j.asoc.2016.07.037.
M. Huan, C. Ming, and Z. Jianwei, “Study on the Prediction of Real estate Price Index based on HHGA-RBF Neural Network Algorithm,” International Journal of u- and e-Service, Science and Technology, vol. 8, no. 7, pp. 109-118, 2015, doi: 10.14257/ijunesst.2015.8.7.11.
J. Weikuan, Z. Dean, S. Tian, et al., “A new optimized GA-RBF neural network algorithm,” Computational Intelligence and Neuroscience, vol. 2014, 2014, doi: 10.1155/2014/982045.
X. Guan and M. Guo, “Application of Improved Fuzzy RBF Neural Network Algorithm in the Prediction of Wood Dyeing Recipes,” International Journal of Applied Mathematics and Statistics, vol. 43, no. 13, pp. 148-154, 2013.
X. Wu, X. G. Li, and B. D. Shan, et al., “RBF Neural Network Arithmetic and Applications in Surface Interpolation Reconstruction,” Key Engineering Materials, vol. 460–461, 2011, doi:10.4028/www.scientific.net/KEM.460-461.575.
H. Chen, R. Y. Fan, and G. S. Deng, “Algorithm Researching of RBF Neural Network Based on Improved PSO,” Advanced Materials Research, vol. 179–180, pp. 233-238, 2011, doi: 10.4028/www.scientific.net/AMR.179-180.233.
M. Nakagawa, T. Kondo, T. Kudo, et al., “Three-dimensional medical image recognition of cancer of the liver by a revised radial basis function (RBF) neural network algorithm,” Artificial Life and Robotics, vol. 14, no. 2, pp. 118-122, 2009, doi: 10.1007/s10015-009-0640-y.
T. Kondo, S. A. Pandya, and H. Nagashino, “GMDH-type neural network algorithm with a feedback loop for structural identification of RBF neural network,” International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 11, no. 3, pp. 157-168, 2007, doi: 10.3233/KES-2007-11302.
P. Longhao, “Research on Forecasting Model of New Retail Sales Volume Based on BP Artificial Neural Network and RBF Neural Network Algorithm,” 2022, doi: 10.2991/AEBMR.K.220307.160.