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首页> 外文会议>The 4th International Conference on Wireless Communications, Networking and Mobile Computing（第四届IEEE无线通信、网络技术及移动计算国际会议）论文集 >Forecasting the Area Flowing of Graduate Employment based on KRNN method
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Forecasting the Area Flowing of Graduate Employment based on KRNN method

机译：基于KRNN的毕业生就业面积预测。

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摘要

It is hard to search the influence variables and to forecast the flowing of graduate employment due to the time series and complex factor inputs. Recently the neural network method has been successfully employed to solve the problem. However the forecasting result is not ideal due to the nonlinearity and noise. In this work, by combining Recurrent Neural Network (RNN) with Kernel Principal Component Analysis (KPCA), a KRNN model is presented, based on which, the area flowing of graduate employment is tried to be forecasted, and both the complex factor problem and time series problem has been dealt with. In the model, RNN with Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA) as the feature extraction is introduced in as comparison. And then by an empirical study with actual data from some high school of China, it is shown that the proposed methods can both achieve good forecasting performance comparing with NN method. And the KPCA method performs better than the PCA method.
机译：由于时间序列和复杂因素的输入，很难搜索影响变量并预测毕业生就业的流动。最近，神经网络方法已经成功地用于解决该问题。然而，由于非线性和噪声，预测结果并不理想。在这项工作中，通过将递归神经网络（RNN）与内核主成分分析（KPCA）结合，提出了一种KRNN模型，在此模型上，试图预测毕业生就业的流动面积，以及复杂因素问题和时间序列问题已得到解决。在模型中，引入了以核主成分分析（KPCA）和主成分分析（PCA）作为特征提取的RNN作为比较。然后通过对某高中实际数据的实证研究，表明所提方法与神经网络方法相比均能达到较好的预测效果。而且，KPCA方法的性能优于PCA方法。

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