Skilled talent evaluation method based on factor analysis and BP neural networks

Abstract

The invention discloses a skilled talent evaluation method based on factor analysis and BP neural networks. The skilled talent evaluation method is characterized in that evaluation index data related to skilled talent evaluation can be acquired, and a total-amount data set can be established; the character-type variables of the total-amount data set can be converted into the numeric-type variables, and then the values of the numeric-type variables can be mapped in a range from 0 to 1 by adopting Sigmoid functions; an evaluation index or an evaluation index combination of the evaluation index can be determined to be participated in the talent evaluation modeling by adopting the above mentioned factor analysis method; the Sigmoid functions can be used as the input vectors of the BP neural networks after being processed, and the task of the BP neural networks is to realize the evaluation of the skilled talent after being trained; and the strategies of different category samples can be trained respectively by adopting a plurality of BP neural networks, and the L-M optimization algorithm can be used to replace the gradient descent method. The skilled talent evaluation method is advantageous in that the skilled talent evaluation costs can be saved, the human resource working efficiency can be improved, and the talent evaluation is more objective and accurate.

Description

technical field [0001] The invention relates to an evaluation method, in particular to a method for evaluating skilled personnel based on factor analysis and BP neural network. Background technique [0002] Skilled talents, as part of scientific and technological talents, are indispensable to the daily operation of enterprises, are crucial to the development of technology-intensive enterprises, and are an important support for the power grid industry. A scientific talent evaluation system is also the prerequisite for the effective development of human resources and the basis for carrying out personnel work. For a long time, the evaluation of technical talents has been more limited to vocational skills appraisal and competition. The evaluation and selection of technical and skilled talents has the shortcomings of "single standard and ignoring the potential of employees." To comprehensively evaluate skilled talents, it is not only necessary to establish a comprehensive evalua...

AI Technical Summary

Problems solved by technology

[0003] The existing talent evaluation method is based on the analytic hierarchy process. Relevant experts are hired to evaluate the talent evaluation indicators. When establishing the evaluation model, the talent evaluation results are obtained by determining the weight of each index. This is a linear evaluation model. Experts with rich experience in the industry evaluate the indicators, the cost is high, the efficiency is low, and the cycle is long

On the other hand, this method is not objective enough, has a certain degree of subjectivity, and the accuracy needs to be improved

The technical model represented by BP neural network is often based on the analytic hierarchy process, which is not objective enough in data collection and selection, insufficient data preprocessing or training method problems, resulting in unsatisfactory model accuracy

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