Title :
Empirical Study on Financial Risk Identification of Chinese Listed Companies Based on ART-2 and SOFM Neural Network Model
Author_Institution :
Manage. Sch., China Univ. of Min. & Technol.(Beijing), Beijing, China
Abstract :
This paper aims at comparing Adaptive Resonance Theory ("ART" for short) and Self-organizing Feature Map ("SOFM" for short) of neural network on the study of Chinese listed company\´s financial risk identification. The empirical results show that the ART-2 neural network model has better recognition effect than Logistic statistical model, BP and PNN network algorithm, while the SOFM network algorithm is better than ART-2.
Keywords :
adaptive resonance theory; financial management; risk management; self-organising feature maps; ART-2; Chinese listed companies; SOFM neural network model; adaptive resonance theory; financial risk identification; self-organizing feature map; Adaptation models; Algorithm design and analysis; Companies; Computational modeling; Neural networks; Neurons; Vectors; ART-2 algorithm; SOFM algorithm; financial risk assessment; listed company; neural network;
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-0-7695-5011-4
DOI :
10.1109/IHMSC.2013.287
