Modelling credit rating by fuzzy adaptive network

• Published in: Mathematical and computer modelling Vol. 45; no. 5; pp. 717 - 731
• Main Authors: Jiao, Yue, Syau, Yu-Ru, Lee, E. Stanley
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.03.2007; Elsevier Science
• Subjects: Applications; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Credit rating; Exact sciences and technology; Fuzzy adaptive network; Fuzzy logic; Fuzzy number; General logic; Insurance, economics, finance; Learning and adaptive systems; Linguistic representation; Logic and foundations; Mathematical logic, foundations, set theory; Mathematics; Operational research and scientific management; Operational research. Management science; Probability and statistics; Sciences and techniques of general use; Statistics; Linguistic representation; Fuzzy logic; Fuzzy number; Credit rating; Fuzzy adaptive network; Statistical estimation; Neural network; Learning; Applied mathematics; Linguistics; Mathematical model; Inference rule; Judgment
• ISSN: 0895-7177; 1872-9479
• DOI: 10.1016/j.mcm.2005.11.016

Human judgment plays an important role in the rating of enterprise financial conditions. The recently developed fuzzy adaptive network (FAN), which can handle systems whose behaviour is influenced by human judgment, appears to be ideally suited for the modelling of this credit rating problem. In this paper, FAN is used to model the credit rating of small financial enterprises. To illustrate the approach, the data of the credit rating problem is first represented by the use of fuzzy numbers. Then, the FAN network based on inference rules is constructed. And finally, the network is trained or learned by using the fuzzy number training data. The main advantages of the proposed network are the ability for linguistic representation, linguistic aggregation and the learning ability of the neural network.