The Research &amp;amp; Application on the Credit Appraisal Model of Architecture Enterprise by the GA-BP Neural Network

Yan, Feng; Fu, Li Ping

doi:10.4028/www.scientific.net/kem.693.2022

Cited by 3 publications

(1 citation statement)

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“…Several scholars have added the Balanced Scorecard to circumvent the current evaluation index method, which exclusively considers financial data. To assess the dynamic capabilities of small-and medium-sized businesses, some researchers created a regional difference coefficient conversion model based on the TOPSIS approach [24][25][26][27][28][29], [30]. Researchers improved the gray correlation degree evaluation of small-and mediumsized enterprises in the east and west by combining the theory that the slopes of two vectors are related to degrees to address the issue of uncoordinated development of dynamic capabilities of enterprises caused by regional differences and external environmental factors.…”

Section: Related Workmentioning

confidence: 99%

Evaluation Method for Diversifying Dynamic Capabilities of Small- and Medium-Sized Enterprises Using a Hybrid Genetic Algorithm

Zhao

2022

Mobile Information Systems

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Focusing on the topic of dynamic capability development and digital transformation of traditional enterprises in the digital economy era, and using traditional manufacturing as an example, this paper examines how small- and medium-sized (SMS) businesses should transform and develop in the digital economy era. Furthermore, in light of the problem that existing models cannot evaluate dynamic nonlinear optimization, a diversified dynamic capability evaluation model of SMS enterprises is developed, and the model is solved using the accelerated genetic value method based on real number coding, while the parameters of the BP network are evaluated using the genetic algorithm. In this study, 100 SMS companies are chosen from a pool of 1000 companies to perform an empirical study. In the empirical study, three different scenarios were employed. The selected dataset is utilized to test BP and GA in the first scenario. In the second scenario, the BP and GA are combined to create a more robust model, which is then evaluated on the specified dataset. This research provides a novel approach that combines the PCA with the acceleration method in the third case. According to the findings of this study, the methodology suggested in this work has an overall evaluation accuracy of 95.6 percent, which is pretty good when compared to other approaches. The model training error and test error, on the other hand, are both lower than those of traditional assessment procedures, according to the data.

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