{"title":"Empirical Study on Financial Risk Identification of Chinese Listed Companies Based on ART-2 and SOFM Neural Network Model","authors":"Guangrong Li","doi":"10.1109/IHMSC.2013.287","DOIUrl":null,"url":null,"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.","PeriodicalId":222375,"journal":{"name":"2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IHMSC.2013.287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
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.