Company Financial Risk Identification Study Based on Several Artificial Neural Networks

Download: PDF (1261 KB) HTML (1 KB) Export: BibTeX | EndNote (RIS) Supporting Info

Abstract This paper aims at finding a method to improve the accuracy degree of company’s financial risk identification. The author puts forward the adaptive resonance theory algorithm and the self-organizing feature map algorithm, which has obvious features of adaptability and self-organizing, respectively to build the company’s financial risk discriminant model for the simulation research. The results show that the recognition accuracy of Adaptive Resonance Theory neural network reaches 87%, and Self-organizing Feature Map network algorithm reaches 89%, both of which have better recognition effects than other Artificial Neural Networks such as BP network algorithm, ect.

	Service

	E-mail this article
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LI Guang-Rong
	LI Feng-Jiang

Key words： Financial Risk Risk Identification Neural Network Adaptive Resonance Theory Self-organizing Feature Map Financial Risk Risk Identification Neural Network Adaptive Resonance Theory Self-organizing Feature Map

Received: 2017-03-30

ZTFLH:

C934