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Predicting technological innovation in new energy vehicles based on an improved radial basis function neural network for policy synergy

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  • Ying Hao
  • Mingshun Guo
  • Yijing Guo

Abstract

Policy synergy is necessary to promote technological innovation and sustainable industrial development. A radial basis function (RBF) neural network model with an automatic coding machine and fractional momentum was proposed for the prediction of technological innovation. Policy keywords for China’s new energy vehicle policies issued over the years were quantified by the use of an Latent Dirichlet Allocation (LDA) model. The training of the neural network model was completed by using policy keywords, synergy was measured as the input layer, and the number of synchronous patent applications was measured as the output layer. The predictive efficacies of the traditional neural network model and the improved neural network model were compared again to verify the applicability and accuracy of the improved neural network. Finally, the influence of the degree of synergy on technological innovation was revealed by changing the intensity of policy measures. This study provides a basis for the relevant departments to formulate industrial policies and improve innovation performance by enterprises.

Suggested Citation

  • Ying Hao & Mingshun Guo & Yijing Guo, 2022. "Predicting technological innovation in new energy vehicles based on an improved radial basis function neural network for policy synergy," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-16, August.
    • Handle: RePEc:plo:pone00:0271316
    DOI: 10.1371/journal.pone.0271316
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    References listed on IDEAS

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