Affiliations: [a] School of Management, Shenyang University of Technology, Shenyang, Liaoning, China | [b] Dean’s Office, Liaoning Engineering Vocational College, Tieling, Liaoning, China | [c] Information Center, Liaoning Engineering Vocational College, Tieling, Liaoning, China
Correspondence:
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Corresponding author: Hui Zeng, Information Center, Liaoning Engineering Vocational College, Tieling, Liaoning 112008, China. E-mail: [email protected].
Abstract: Considering the influence of new energy vehicle enterprises innovation input is affected by a variety of non-linear and uncertain factors, an automatic coding machine mixed with RBF neural network model is presented in this paper, and the Gaussian distribution of training data optimization method and the Gaussian transfer function training module are put forward to make innovation input higher prediction precision and stronger universality. By comparing the prediction data of the proposed model with that of the traditional neural network model, the accuracy of the improved model is verified. Therefore, the proposed model can provide theoretical basis and decision support for technological innovation decision-making of new energy vehicle enterprises.
Keywords: Hybrid neural network, gaussian distribution, new energy vehicle, innovation input, prediction
