基于改进深度动态模糊神经网络的信息综合分析算法

Information synthesis analysis algorithm based on improved depth dynamic fuzzy neural network

针对传统人力资源评价算法的主观性强,难以反映员工真实能力的问题,提出了一种结合深度动态模糊神经网络和粒子群优化的算法。该算法对传统模糊神经网络进行了改进,并使用动态结构来增强原模型训练能力,通过对隶属函数层的优化,使模型具备了处理广域数据的能力。为了提高算法的运行效率,还采用误差下降法对模型的规则权重进行排序并完成剪枝操作,同时利用粒子群算法实现对模型参数的优化。实验测试结果表明,所提算法的训练时间仅需7.8 s,性能与效率指标则均优于对比算法,且与人工评价法得到的指标大致相同,可以作为电力人才评价的辅助数据参考。

In view of the subjectivity of traditional human resource evaluation algorithm,which is difficult to reflect the real ability of employees,an algorithm combining deep dynamic fuzzy neural network and particle swarm optimization is proposed.The algorithm improves the traditional fuzzy neural network,and uses the dynamic structure to enhance the training ability of the original model.Through the optimization of the membership function layer,the model has the ability to process wide area data.In order to improve the operation efficiency of the algorithm,the error descent method is also used to sort the rule weights of the model and complete the pruning operation.At the same time,the particle swarm optimization algorithm is used to optimize the model parameters.The experimental results show that the training time of the proposed algorithm is only 7.8 s,and the performance and efficiency indicators are better than those of the comparison algorithm,and the indicators obtained by the manual evaluation method are roughly the same,which can be used as the auxiliary data reference for the evaluation of electric power talents.