Chengdu Sport University, Chengdu, Sichuan 610041, China.
Business School, Sichuan University, Chengdu, Sichuan 610064, China.
Comput Intell Neurosci. 2021 Nov 6;2021:3133065. doi: 10.1155/2021/3133065. eCollection 2021.
With the continuous development of social economy and the intensification of social competition, human resource management plays a more and more important role in the whole resource system. How to give full play to the advantages of human resources has become the key issue of human resource management evaluation. However, the current human resource management evaluation system has some problems, such as poor timeliness, one-sidedness, and subjectivity. Therefore, this paper proposes a BP image neural network optimized based on the simulated annealing algorithm to realize enterprise human resource management evaluation and image analysis. Through the learning of different time series samples, the average weight distribution scheme of main indicators is obtained, in which the average weight proportions of , , , and are 25.5%, 24.8%, 17.9%, and 31.9%, respectively. In the comprehensive evaluation of enterprise employees, the error between the actual output and expected output is less than 4.5%. The results show that the BP image neural network based on simulated annealing algorithm has high accuracy in the image analysis and evaluation of enterprise human resource management. The output analysis results meet the actual needs of the enterprise and the personal development of employees and provide a decision-making scheme for the evaluation of enterprise human resource management.
随着社会经济的不断发展和社会竞争的加剧，人力资源管理在整个资源系统中发挥着越来越重要的作用。如何充分发挥人力资源的优势，已成为人力资源管理评价的关键问题。然而，现行的人力资源管理评价体系存在时效性差、片面性和主观性等问题。为此，本文提出了一种基于模拟退火算法优化的 BP 图像神经网络，以实现企业人力资源管理评价和图像分析。通过对不同时间序列样本的学习，得到主要指标的平均权重分布方案，其中 、 、 、 的平均权重比例分别为 25.5%、24.8%、17.9%和 31.9%。在企业员工的综合评价中，实际输出与预期输出的误差小于 4.5%。结果表明，基于模拟退火算法的 BP 图像神经网络在企业人力资源管理的图像分析和评价中具有较高的准确性。输出分析结果符合企业的实际需求和员工的个人发展，并为企业人力资源管理评价提供了决策方案。
