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

Jianzhong Li
Jianzhong Li
   | 05. Sept. 2022
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Online veröffentlicht: 05. Sept. 2022
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Eingereicht: 11. Apr. 2022
Akzeptiert: 14. Apr. 2022
DOI: https://doi.org/10.2478/amns.2022.1.00036
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© 2022 Jianzhong Li, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Principles of enterprise economic benefit evaluation.
Principles of enterprise economic benefit evaluation.

Fig. 2

Traditional economic benefit analysis method.
Traditional economic benefit analysis method.

Fig. 3

Weakness of traditional economic benefit analysis methods.
Weakness of traditional economic benefit analysis methods.

Fig. 4

Steps of kernel principal component analysis.
Steps of kernel principal component analysis.

Evaluation index of economic benefits of enterprises.

Number Total output value (ten thousand yuan) Product sales rate (%) Sales revenue (ten thousand yuan) Total profit (ten thousand yuan) Total profits and taxes (ten thousand yuan)
A 1500 100.6 1172 25 67
B 1002 105.3 954 80 76
C 4890 83.9 2754 198 407
D 20,205 94.2 22082 1021 983
E 589 98.0 674 493 26

The contribution rate of each characteristic value under polynomial kernel function.

X1 Total output value (ten thousand yuan) X2 Product sales rate (%) X3 Sales revenue (ten thousand yuan)
Eigenvalue 1.127 0.063 0.003
Contribution rate 93.98% 4.75% 1.27%
Cumulative contribution rate 93.98% 98.73% 100%

The contribution rate of enterprise economic benefit under Gaussian kernel function when σ = 50.

Eigenvalue Contribution rate Cumulative contribution rate
X1 0.286 45.65% 45.65%
X2 0.203 22.58% 68.23%
X3 0.128 19.59% 87.82%
X4 0.059 7.51% 95.33%
X5 0.012 4.67% 100%

Contribution rate of enterprise economic benefit under neural network kernel function.

Eigenvalue Contribution rate Cumulative contribution rate
X1 0.016 52.38% 52.38%
X2 0.008 41.82% 94.20%
X3 0.001 5.80% 100%

Contribution rate of enterprise economic benefit under mixed kernel function.

Eigenvalue Contribution rate Cumulative contribution rate
X1 0.016 99.80 99.80%
X2 0.008 0.2% 100%

Standardisation results of evaluation index data.

Number X1 Total output value (ten thousand yuan) X2 Product sales rate (%) X3 Sales revenue (ten thousand yuan) X4 Total profit (ten thousand yuan) X5 Total profits and taxes (ten thousand yuan)
A −0.267 0.642 −0.239 −0.593 −0.502
B 0.557 0.901 0.583 −0.237 −0.347
C −0.429 −1.290 −0.493 −0.320 −0.352
D 3.278 1.829 3.512 3.290 3.200
E −0.015 −0.483 −0.089 −0.072 −0.126

Ranking of Economic Benefits of Enterprises.

Enterprise number Evaluation coefficient Ranking
A 0.837 2
B −0.509 4
C −0.378 3
D 3.233 1
E −0.835 5
eISSN:
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Sprache:
Englisch
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Fachgebiete der Zeitschrift:
Biologie, andere, Mathematik, Angewandte Mathematik, Allgemeines, Physik
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