Construction of a GA-RBF-based early warning model for corporate financial risk in the context of sustainable development
by Jingjing Gong; Hongwen Han; Zhen Lv
International Journal of Networking and Virtual Organisations (IJNVO), Vol. 28, No. 2/3/4, 2023

Abstract: Financial risk indicators will have a negative impact on the development planning of enterprises, so the research introduces the theory of genetic algorithm. The result is that the overall performance of the model based on GA-RBF is superior to that of the model based on BF, CNN and RBF. GA-RBF model reaches a stable state when the number of training is 120, and the speed is significantly faster than the other three models. The error value of GA-RBF model is significantly lower than other model, and the error reduction speed is also faster. The time and memory of the four models increase with the increase of the number of samples, but the time and memory of GA-RBF model is less than the other three models. The highest prediction accuracy of GA-RBF model is 91.25%, and the highest prediction accuracy of RBF neural network is 64.5%.

Online publication date: Wed, 04-Oct-2023

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Networking and Virtual Organisations (IJNVO):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com