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Evaluation of Regional Economic Innovation Ability Based on Neural Network

In order to further improve regional economic innovation capability and governance level and solve the problems of lack of attention to evaluation indicators in traditional evaluation methods of regional economic innovation capability and easy to be affected by subjective factors, an evaluation mode...

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Detalles Bibliográficos
Autores principales: Tingting, Song, Jiaying, Wang, Nan, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9578852/
https://www.ncbi.nlm.nih.gov/pubmed/36268142
http://dx.doi.org/10.1155/2022/8198453
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author Tingting, Song
Jiaying, Wang
Nan, Feng
author_facet Tingting, Song
Jiaying, Wang
Nan, Feng
author_sort Tingting, Song
collection PubMed
description In order to further improve regional economic innovation capability and governance level and solve the problems of lack of attention to evaluation indicators in traditional evaluation methods of regional economic innovation capability and easy to be affected by subjective factors, an evaluation model based on neural network algorithm is proposed. Through re-analysis of regional economic innovation capability evaluation indexes, the model defines the most reasonable combination of characteristics by combining information gain characteristic selection strategy and finally builds a scientific evaluation index system. By testing the prediction accuracy of the experimental discovery model and evaluation index, the neural network model improves by 41% compared with the traditional subjective evaluation method, and the accuracy increases by 20% compared with the GA-BP neural network model. The experiment proves the stability and good convergence effect of the evaluation model.
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spelling pubmed-95788522022-10-19 Evaluation of Regional Economic Innovation Ability Based on Neural Network Tingting, Song Jiaying, Wang Nan, Feng Comput Intell Neurosci Research Article In order to further improve regional economic innovation capability and governance level and solve the problems of lack of attention to evaluation indicators in traditional evaluation methods of regional economic innovation capability and easy to be affected by subjective factors, an evaluation model based on neural network algorithm is proposed. Through re-analysis of regional economic innovation capability evaluation indexes, the model defines the most reasonable combination of characteristics by combining information gain characteristic selection strategy and finally builds a scientific evaluation index system. By testing the prediction accuracy of the experimental discovery model and evaluation index, the neural network model improves by 41% compared with the traditional subjective evaluation method, and the accuracy increases by 20% compared with the GA-BP neural network model. The experiment proves the stability and good convergence effect of the evaluation model. Hindawi 2022-10-11 /pmc/articles/PMC9578852/ /pubmed/36268142 http://dx.doi.org/10.1155/2022/8198453 Text en Copyright © 2022 Song Tingting et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Tingting, Song
Jiaying, Wang
Nan, Feng
Evaluation of Regional Economic Innovation Ability Based on Neural Network
title Evaluation of Regional Economic Innovation Ability Based on Neural Network
title_full Evaluation of Regional Economic Innovation Ability Based on Neural Network
title_fullStr Evaluation of Regional Economic Innovation Ability Based on Neural Network
title_full_unstemmed Evaluation of Regional Economic Innovation Ability Based on Neural Network
title_short Evaluation of Regional Economic Innovation Ability Based on Neural Network
title_sort evaluation of regional economic innovation ability based on neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9578852/
https://www.ncbi.nlm.nih.gov/pubmed/36268142
http://dx.doi.org/10.1155/2022/8198453
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