深度置信网络优化模型在人才评价中的应用

针对深度置信网络(DBN)权值随机初始化容易使网络陷入局部最优的问题,引入改进的和声搜索(IHS)算法,提出基于IHS的DBN模型(IHS-DBN)。在和声搜索算法的基础上,利用全局自适应的和声音调调整方式,提升算法收敛速度和后期局部搜索能力。将DBN重构误差函数作为IHS算法的优化目标函数,通过不断迭代优化解向量为DBN寻找一组较优的初始权值进行网络训练,并在MNIST数据集上验证IHS-DBN模型的有效性。IHS-DBN模型在高校人才评价中的应用结果表明,与DBN、SVM和BP神经网络评价模型相比,IHS-DBN模型的评价准确率分别提高3.6%、7.3%和16.4%。

Application of Optimized Deep Belief Network Model in Talent Evaluation

Traditional Deep Belief Networks(DBN)are often limited within local optimum due to the random initialization of weights.To address the problem,this paper introduces an improved Improved Harmony Search(IHS)algorithm into traditional DBN to construct an IHS-based DBN model,called IHS-DBN.Firstly,to improve the convergence speed and local search ability of the Harmony Search(HS)algorithm,the globally adaptive method of adjusting harmony tone is used.Secondly,the reconstruction error function of DBN is taken as the optimization objective function of the IHS algorithm.Then the solution vector is iteratively optimized to find a set of more optimal initial weights for DBN training.The proposed model is tested on the MNIST dataset for talent evaluation in colleges to verify its effectiveness.Results show that the accuracy of the HIS-DBN model is improved by 3.6%,7.3%and 16.4%respectively compared with DBN,SVM and BP neural network evaluation models.