技术经济与管理研究
技術經濟與管理研究
기술경제여관리연구
TECHNOECONOMICS & MANAGEMENT RESEARCH
2014年
2期
45-50
,共6页
心理契约%神经网络%员工管理%企业管理
心理契約%神經網絡%員工管理%企業管理
심리계약%신경망락%원공관리%기업관리
Psychological contract%Neural network%Staff management%Enterprise management
以知识型员工心理契约为研究对象,结合在国内三家知识型员工企业调研的实测数据,建立基于RBF神经网络的心理契约预测模型,同时为实现对RBF神经网络预测效率的优化,选择回归树与RBF神经网络相结合的方式,力求实现两者的优势互补,建立一个高效便捷的回归树与RBF神经网络相结合的知识型员工心理契约预测模型。结果表明:通过回归树、 RBF神经网络预测数据结果与在三家知识型员工企业实际施测数据结果比较后发现,回归树与RBF结合的神经网络预测数据结果具有较高的准确性。因此,我们可以认为基于回归树的RBF神经网络的学习算法和以该算法为核心的知识型员工心理契约水平预测模型是有效的,该模型对知识型员工心理契约水平的预测具有较高的准确性。该模型能够替代以往对知识型员工心理契约主观预测的方法,使心理契约的预测过程更为高效,预测结果更加科学。
以知識型員工心理契約為研究對象,結閤在國內三傢知識型員工企業調研的實測數據,建立基于RBF神經網絡的心理契約預測模型,同時為實現對RBF神經網絡預測效率的優化,選擇迴歸樹與RBF神經網絡相結閤的方式,力求實現兩者的優勢互補,建立一箇高效便捷的迴歸樹與RBF神經網絡相結閤的知識型員工心理契約預測模型。結果錶明:通過迴歸樹、 RBF神經網絡預測數據結果與在三傢知識型員工企業實際施測數據結果比較後髮現,迴歸樹與RBF結閤的神經網絡預測數據結果具有較高的準確性。因此,我們可以認為基于迴歸樹的RBF神經網絡的學習算法和以該算法為覈心的知識型員工心理契約水平預測模型是有效的,該模型對知識型員工心理契約水平的預測具有較高的準確性。該模型能夠替代以往對知識型員工心理契約主觀預測的方法,使心理契約的預測過程更為高效,預測結果更加科學。
이지식형원공심리계약위연구대상,결합재국내삼가지식형원공기업조연적실측수거,건립기우RBF신경망락적심리계약예측모형,동시위실현대RBF신경망락예측효솔적우화,선택회귀수여RBF신경망락상결합적방식,력구실현량자적우세호보,건립일개고효편첩적회귀수여RBF신경망락상결합적지식형원공심리계약예측모형。결과표명:통과회귀수、 RBF신경망락예측수거결과여재삼가지식형원공기업실제시측수거결과비교후발현,회귀수여RBF결합적신경망락예측수거결과구유교고적준학성。인차,아문가이인위기우회귀수적RBF신경망락적학습산법화이해산법위핵심적지식형원공심리계약수평예측모형시유효적,해모형대지식형원공심리계약수평적예측구유교고적준학성。해모형능구체대이왕대지식형원공심리계약주관예측적방법,사심리계약적예측과정경위고효,예측결과경가과학。
With the psychological contract for the knowledgeable employees as the object, and the measured data from the re-search in three knowledgeable staff domestic enterprises, the predictive model of psychological contract based on RBF neural ne-twork is built. At the same time, an efficient and convenient regression tree combined with RBF neural network predictive model with the knowledgeable employees' psychological contract is established , in order to realize the optimization of RBF neural network prediction efficiency, select regression tree combined with RBF neural network, strive to achieve the complementary advantages of both. The results shows that through regression tree with RBF neural network prediction results compared with the actual measured data in three knowledge staff enterprises, the regression tree combined with RBF neural network prediction resu-lts has higher accuracy. Therefore, the learning algorithm of the regression tree-based RBF neural network and the prediction model for the algorithm-cored employees' psychological contract level prediction model are efficient.This model substitutes the pr-evious subjective measuring methods of psychological contract, meanwhile the establishment of the model provides managers with a scientific and efficient method for the forecast of the psychological contract for knowledgeable employee. The model to predict the level of the psychological contract for knowledgeable employee has higher accuracy.