Application of hybrid kernel function in economic benefit analysis and evaluation of enterprises

Author:

Li Jianzhong1

Affiliation:

1. College of Economics and Management, Hubei University of Automotive Technology , Shiyan, Hubei , China

Abstract

Abstract The economic benefit of enterprises is an effective index to measure the economic activities, which forms the basis and starting point. Therefore, in this paper, the traditional analysis methods of enterprise in terms of economic benefits are compared and based on its evaluation principles, the advantages, theory and realisation process of the kernel function in enterprise economic benefits analysis are analysed. Then, different kernel functions are selected to analyse the total output value, product sales rate, sales revenue, total profit and total profits after taxes of five enterprises, and their economic benefits are evaluated by the cumulative contribution rate of principal components. Finally, the mixed kernel function based on the combination of polynomial kernel function and Gaussian kernel function is used to optimise the analysis effect, which is meant to help enterprise leaders make more scientific decisions and lay a foundation for the sound development of enterprises.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science

Reference22 articles.

1. Ye Hua. Predicament and Solution of Enterprise Economic Benefit Audit [J]. Times Finance, 2021, (12), 47–49. (in Chinese)

2. Liu Fengming. Talking about How to Effectively Improve the Economic Benefits of Enterprises by Means of Accounting Audit [J]. Financial and Economic Circle, 2021, (12), 148–149. (in Chinese)

3. Song Jia. The Application of Multivariate Statistical Analysis in the Economic Benefits of Enterprises [J]. Productivity Research, 2020, (3), 141–143. (in Chinese)

4. Li Rende, Huang Guoan. Economic Benefit Evaluation of Enterprises Based on Structural Equation Model. Technology and Innovation Management, 2010, (4), 451–454. (in Chinese)

5. Aminian F, Suarez E D, Aminian M, et al. Forecasting Economic Data with Neural Networks [J]. Computational Economics, 2016, 28(1), 71–88.

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