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Research on the Evaluation Method of Enterprises' Independent Innovation Ability Based on Improved BP Neural Network and DQN Algorithm.
Comput Intell Neurosci. 2022; 2022:8250879.CI

Abstract

The development of enterprises has a very important influence on promoting national economic growth and improving comprehensive economic strength. This work evaluates the independent innovation ability of enterprises, analyzes the characteristics and difficulties of technological innovation of enterprises, and proposes corresponding solutions to promote independent technological innovation of enterprises. Firstly, the characteristics of the research object are clarified, and on the basis of relevant research, the theory of technological innovation and evaluation at home and abroad is expounded. At the same time, the basic theory of the improved BP neural network and DQN algorithm is introduced, which provides a theoretical basis for the research of the thesis. Secondly, according to the characteristics of enterprise technological innovation, an index system for evaluating the technological innovation capability of enterprises is constructed. Then, according to the related theory of the improved BP neural network and DQN algorithm, a neural network model for evaluating the technological innovation capability of enterprises is designed, and the validity of the model is verified through empirical research. Finally, this paper applies the evaluation model to the surveyed enterprises, comprehensively analyzes the characteristics and existing problems of independent technological innovation of enterprises, and proposes practical and feasible countermeasures to improve technological innovation capabilities from the perspective of enterprises themselves. The research results of this paper can be used as an effective supplement to the research on independent technological innovation of enterprises, and at the same time promote the continuous improvement of independent technological innovation capabilities of enterprises.

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Authors+Show Affiliations

Fan Y
College of Management, Ocean University of China, Qingdao, Shandong 266100, China.
Ding D
College of Foreign Languages, Shangqiu Normal University, Shangqiu, Henan 476000, China.
Qin H
College of Management, Ocean University of China, Qingdao, Shandong 266100, China.

MeSH

AlgorithmsInventionsNeural Networks, Computer

Pub Type(s)

Journal Article
Retracted Publication

Language

eng

PubMed ID

35371243

Citation

Fan, Yipin, et al. "Research On the Evaluation Method of Enterprises' Independent Innovation Ability Based On Improved BP Neural Network and DQN Algorithm." Computational Intelligence and Neuroscience, vol. 2022, 2022, p. 8250879.
Fan Y, Ding D, Qin H. Research on the Evaluation Method of Enterprises' Independent Innovation Ability Based on Improved BP Neural Network and DQN Algorithm. Comput Intell Neurosci. 2022;2022:8250879.
Fan, Y., Ding, D., & Qin, H. (2022). Research on the Evaluation Method of Enterprises' Independent Innovation Ability Based on Improved BP Neural Network and DQN Algorithm. Computational Intelligence and Neuroscience, 2022, 8250879. https://doi.org/10.1155/2022/8250879
Fan Y, Ding D, Qin H. Research On the Evaluation Method of Enterprises' Independent Innovation Ability Based On Improved BP Neural Network and DQN Algorithm. Comput Intell Neurosci. 2022;2022:8250879. PubMed PMID: 35371243.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Research on the Evaluation Method of Enterprises' Independent Innovation Ability Based on Improved BP Neural Network and DQN Algorithm. AU - Fan,Yipin, AU - Ding,Ding, AU - Qin,Hong, Y1 - 2022/03/24/ PY - 2022/01/14/received PY - 2022/02/23/revised PY - 2022/03/05/accepted PY - 2022/4/4/entrez PY - 2022/4/5/pubmed PY - 2022/4/6/medline SP - 8250879 EP - 8250879 JF - Computational intelligence and neuroscience JO - Comput Intell Neurosci VL - 2022 N2 - The development of enterprises has a very important influence on promoting national economic growth and improving comprehensive economic strength. This work evaluates the independent innovation ability of enterprises, analyzes the characteristics and difficulties of technological innovation of enterprises, and proposes corresponding solutions to promote independent technological innovation of enterprises. Firstly, the characteristics of the research object are clarified, and on the basis of relevant research, the theory of technological innovation and evaluation at home and abroad is expounded. At the same time, the basic theory of the improved BP neural network and DQN algorithm is introduced, which provides a theoretical basis for the research of the thesis. Secondly, according to the characteristics of enterprise technological innovation, an index system for evaluating the technological innovation capability of enterprises is constructed. Then, according to the related theory of the improved BP neural network and DQN algorithm, a neural network model for evaluating the technological innovation capability of enterprises is designed, and the validity of the model is verified through empirical research. Finally, this paper applies the evaluation model to the surveyed enterprises, comprehensively analyzes the characteristics and existing problems of independent technological innovation of enterprises, and proposes practical and feasible countermeasures to improve technological innovation capabilities from the perspective of enterprises themselves. The research results of this paper can be used as an effective supplement to the research on independent technological innovation of enterprises, and at the same time promote the continuous improvement of independent technological innovation capabilities of enterprises. SN - 1687-5273 UR - https://www.unboundmedicine.com/medline/citation/35371243/Research_on_the_Evaluation_Method_of_Enterprises'_Independent_Innovation_Ability_Based_on_Improved_BP_Neural_Network_and_DQN_Algorithm_ DB - PRIME DP - Unbound Medicine ER -