<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With economic globalisation and in the face of increasingly fierce market competition, enterprises must establish an evaluation mechanism that can promote the company’s development and motivate employees to improve performance in order to achieve the company’s strategic goals. In view of the characteristics and problems of enterprise performance appraisal, a performance evaluation method based on artificial neural network (ANN) technology is proposed. This study uses BP algorithm to comprehensively evaluate the performance of enterprises and construct an evaluation network. According to the statistics of 75 power companies in the province from 2018 to 2021, the training was carried out in batches, and the 10-fold cross-validation method was used to find the smallest optimisation value of the error term (average overall relative error) of the test set. The training set is set as 70% and the test set as 30%, and the termination condition is used to end the training process when the training error is &lt;0.0001. This proves that the use of BP neural network for performance evaluation can effectively avoid the influence of subjective factors on the evaluation results, so as to establish a more objective comprehensive evaluation system.</p>
</abstract>

With economic globalisation and in the face of increasingly fierce market competition, enterprises must establish an evaluation mechanism that can promote the company’s development and motivate employees to improve performance in order to achieve the company’s strategic goals. In view of the characteristics and problems of enterprise performance appraisal, a performance evaluation method based on artificial neural network (ANN) technology is proposed. This study uses BP algorithm to comprehensively evaluate the performance of enterprises and construct an evaluation network. According to the statistics of 75 power companies in the province from 2018 to 2021, the training was carried out in batches, and the 10-fold cross-validation method was used to find the smallest optimisation value of the error term (average overall relative error) of the test set. The training set is set as 70% and the test set as 30%, and the termination condition is used to end the training process when the training error is <0.0001. This proves that the use of BP neural network for performance evaluation can effectively avoid the influence of subjective factors on the evaluation results, so as to establish a more objective comprehensive evaluation system.

Research on human resource management performance evaluation based on BP algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With economic globalisation and in the face of increasingly fierce market competition, enterprises must establish an evaluation mechanism that can promote the...

Research on human resource management performance evaluation based on BP algorithm

Publicado en línea: 10 abr 2023

Páginas: 1155 - 1166

Recibido: 29 may 2022

Aceptado: 08 jul 2022

DOI: https://doi.org/10.2478/amns.2021.2.00302

Palabras clave
BP algorithm, neural network, human resource management, the performance evaluation

© 2023 Rui Lv, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on human resource management performance evaluation based on BP algorithm

Publicado en línea: 10 abr 2023

Páginas: 1155 - 1166

Recibido: 29 may 2022

Aceptado: 08 jul 2022

DOI: https://doi.org/10.2478/amns.2021.2.00302

Palabras claveBP algorithm, neural network, human resource management, the performance evaluation

© 2023 Rui Lv, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
BP algorithm, neural network, human resource management, the performance evaluation