Abstract
Current industry talent demand forecast has a high complexity and non-linearity. The gray method GM(1,1) is suitable to deal with the problem of uncertain forecast with low richness of historical data without consistency, and Back-Propagation Neural Network model (BPNN) is adopted to analyze the influence of current influencing factors. The application of the improved gray-neural network integrated forecasting model in the complex nonlinear forecast is studied in combination with the case of talent demand of a certain city in northern China. Combining the GM(1,1) model, metabolism and background value optimization as the Improved Metabolic GM (1,1) model (IMGM), the forecast result of IMGM is used as the input to train BPNN for improving the forecast accuracy. And the computer simulation flow of talent demand is designed. The result of modeling example shows that the accuracy of improved integrated forecasting model IMGM-BPNN is higher than the conventional model’s.
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References
Xia, J., Xu, H.: Forecasting tourism income of Yunnan province by gray neural network. Value Eng. 37(21), 104–108 (2018)
Yuan, P.-w, Song, S.-x, Dong, X.-q: Study on fire accident prediction based on optimized grey neural network combination model. J. Saf. Sci. Technol. 10(3), 119–124 (2014)
Zecchin, C., Facchinetti, A., Sparacino, G., De Nicolao, G., Cobelli, C.: A new neural network approach for short-term glucose prediction using continuous glucose monitoring time-series and meal information. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5653–5656 (2011)
Wang, L.P., Teo, K.K., Lin, Z.P.: Predicting time series with wavelet packet neural networks. In: 2001 IEEE International Joint Conference on Neural Networks (IJCNN 2001), pp. 1593–1597 (2001)
Ren, Y., Suganthan, P.N., Srikanth, N., Amaratunga, G.: Random vector functional link network for short-term electricity load demand forecasting. Inf. Sci. 367–368, 1078–1093 (2016)
Zhu, M., Wang, L.P.: Intelligent trading using support vector regression and multilayer perceptrons optimized with genetic algorithms. In: 2010 International Joint Conference on Neural Networks (IJCNN 2010) (2010)
Song, D.: Analysis on the demand and training mode of professionals in strategic emerging industries – taking Jiangsu province as an example. Reform Opening up 9, 56–59 (2015)
Yang, L., He, Z., Han, F.: Strategic emerging industry talent demand forecast and countermeasures for Hunan province. Forum Sci. Technol. China 1(11), 85–91 (2013)
Min, X.: Talent demand analysis based on gray prediction model GM(1,1). Sci. Technol. Manag. Res. 6, 25 (2005)
Wang, C.: Construction of talent demand forecasting model for strategic emerging industries – taking Jiangsu province as an example. Sci. Technol. Assoc. Forum (second half), 2, 141–142 (2012)
Wang, N., Zhang, S., Zeng, Q.: Research on aging prediction of Chongqing population based on metabolic GM(1,1) model. Northwest Popul. J. 38(1), 66–70 (2017)
Liu, L., Wang, H., Wang, B.: GM(1,1) model optimization based on background value construction method. Stat. Decis. 277(1), 153–155 (2009)
Kong, X., Wang, L., Feng, Y.-h.: Review and prospects of the application status of GM (1,1). J. Qilu Univ. Technol. 32(6), 49–53 (2018)
Wang, H.: Research on gray neural network prediction model. North China University of Water Resources and Electric Power
Wei, Y., Yuxiang, L.: Manpower demand forecasting of strategic emerging industry in china: based on grey system methodology. In: Portland International Conference on Management of Engineering & Technology. IEEE (2015)
O’Brien-Pallas, L., Baumann, A., Donner, G., et al.: Forecasting models for human resources in health care. J. Adv. Nurs. 33(1), 10 (2001)
Goyal, V., Deolia, V.K., Sharma, T.N.: Robust sliding mode control for nonlinear discrete-time delayed systems based on neural network. Intell. Control Autom. 1, 75–83 (2015)
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Huang, G., Zhang, Z., Zhang, J. (2020). Improved Gray-Neural Network Integrated Forecasting Model Applied in Complex Forecast. In: Kim, J., Geem, Z., Jung, D., Yoo, D., Yadav, A. (eds) Advances in Harmony Search, Soft Computing and Applications. ICHSA 2019. Advances in Intelligent Systems and Computing, vol 1063. Springer, Cham. https://doi.org/10.1007/978-3-030-31967-0_1
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DOI: https://doi.org/10.1007/978-3-030-31967-0_1
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