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International Conference on Harmony Search Algorithm

ICHSA 2019: Advances in Harmony Search, Soft Computing and Applications pp 1–15Cite as

Improved Gray-Neural Network Integrated Forecasting Model Applied in Complex Forecast

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1063)

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.

Keywords

  • IMGM-BPNN
  • Improved Metabolic GM(1,1)
  • Talent demand forecast

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Authors and Affiliations

  1. Engineering University of PAP, Weiyang District, Xi’an, 710086, China

    Geyu Huang, Zhiming Zhang & Jun Zhang

Authors
  1. Geyu Huang
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  2. Zhiming Zhang
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  3. Jun Zhang
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Corresponding author

Correspondence to Geyu Huang .

Editor information

Editors and Affiliations

  1. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Prof. Dr. Joong Hoon Kim

  2. Department of Energy IT , Gachon University, Seongnam, Kwangju-jikhalsi, Korea (Republic of)

    Zong Woo Geem

  3. Keimyung University, Daegu, Korea (Republic of)

    Prof. Donghwi Jung

  4. Department of Civil Engineering, Suwon University, Gyeonggi-do, Korea (Republic of)

    Dr. Do Guen Yoo

  5. Department of Mathematics, Dr. BR Ambedkar National Institute of Technology, Jalandhar, India

    Dr. Anupam Yadav

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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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