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E. P. Dadios, K. Hirota, M. Catigum, A. Gutierrez, D. Rodrigo, C, San Juan and J. Tan, “Neural Network Vision Guided Mobile Robot for Driving Range Golf Ball Retriever,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 10, No. 4, 2006, pp. 181185.

has been cited by the following article:

• TITLE: Fuzzy-Neuro Model for Intelligent Credit Risk Management

  AUTHORS: Elmer P. Dadios, James Solis

  KEYWORDS: Fuzzy Logic; Neural Networks; Fuzzy-Neuro Model; Credit Risk Management

  JOURNAL NAME: Intelligent Information Management, Vol.4 No.5A, October 30, 2012

  ABSTRACT: This paper presents hybrid fuzzy logic and neural network algorithm to solve credit risk management problem. Credit risk is the risk of loss due to a debtor’s non-payment of a loan or other line of credit. A method of evaluating the credit worthiness of a customer is complex and non-linear due to the diverse combinations of risk involve. To address this problem a credit scoring method is proposed in this paper using hybrid fuzzy logic-neural network (HFNN) model. The model will be implemented, tested, and validated for individual auto loans using real life bank data. The neural network is used as the learner and the fuzzy logic is used as the implementer. The neural network will fine tune the fuzzy sets, remove redundant input variables, and extract fuzzy rules. The extracted fuzzy rules are evaluated to retain the best k number of rules that will give final and intelligent decisions. The experiment results show that the perform-ance of the proposed HFNN model is very accurate, robust, and reliable. Comparison of these results to other previous published works is also presented in this paper.