Innovation prediction of new energy vehicle enterprises based on improved hybrid neural network model

Ying Hao; Ming-Shun Guo; Hui Zeng

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Innovation prediction of new energy vehicle enterprises based on improved hybrid neural network model

机译：Innovation prediction of new energy vehicle enterprises based on improved hybrid neural network model

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摘要

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.

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来源
《Journal of computational methods in sciences and engineering》 |2022年第6期|2413-2423|共11页
作者
Ying Hao; Ming-Shun Guo; Hui Zeng;
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作者单位

School of Management, Shenyang University of Technology, Shenyang, Liaoning, China, Dean 's Office, Liaoning Engineering Vocational College, Tieling, Liaoning, China;

School of Management, Shenyang University of Technology, Shenyang, Liaoning, China;

Information Center, Liaoning Engineering Vocational College, Tieling, Liaoning, China;

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原文格式 PDF
正文语种英语
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关键词
Hybrid neural network; gaussian distribution; new energy vehicle; innovation input; prediction;

Innovation prediction of new energy vehicle enterprises based on improved hybrid neural network model

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