Contender's network, a new competitive-learning scheme
Pattern Recognition Letters
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
POPFNN: a pseudo outer-product based fuzzy neural network
Neural Networks
Data equalisation with evidence combination for pattern recognition
Pattern Recognition Letters
Basis function models of the CMAC network
Neural Networks
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Pattern Recognition Letters
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Artificial Intelligence in Medicine
Neural Networks - 2005 Special issue: IJCNN 2005
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms
Expert Systems with Applications: An International Journal
POPFNN-AAR(S): a pseudo outer-product based fuzzy neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Falcon: neural fuzzy control and decision systems using FKP and PFKP clustering algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections
IEEE Transactions on Neural Networks
A nature inspired Ying-Yang approach for intelligent decision support in bank solvency analysis
Expert Systems with Applications: An International Journal
A study of financial insolvency prediction model for life insurers
Expert Systems with Applications: An International Journal
HebbR2-Taffic: A novel application of neuro-fuzzy network for visual based traffic monitoring system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy associative conjuncted maps network
IEEE Transactions on Neural Networks
RFCMAC: A novel reduced localized neuro-fuzzy system approach to knowledge extraction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In the banking industry, it is highly desirable to identify potential bank failure or high-risk banks. Successful early warning systems (EWS) would provide capabilities to avoid adverse financial repercussions and a massive bail out costs for the failing banks. Very often, these failures are due to financial distress. Various traditional statistical models have been used to study failures of financial institutions (Sinkey, J., Jr. (1975). A multivariate statistical analysis of the characteristics of problem banks. Journal of Finance, 1, 21-36; Martin, D. (1977). Early warning of bank failure: A logit regression approach. Journal of Banking and Finance, 1(3), 249-276; Lane, W., Looney, S., & Wansley, J. (1986). An application of the Cox proportional hazards model to bank failure. Journal of Banking and Finance, 10, 511-531; Cole, R., & Gunther, J. (1995). Separating the likelihood and timing of bank failure. Journal of Banking and Finance, 19, 1073-1089.). However, these models do not have the capability to identify the characteristics of financial distress and thus function as black boxes. This paper proposes a novel fuzzy CMAC (cerebellar model articulation controller) model based on compositional rule of inference, named FCMAC-CRI(S), as a new approach to tackle the problem using localized learning. The CRI-based FCMAC network, based on localized training, is able to identify the inherent traits and patterns of financial distress based on financial covariates derived from publicly available financial statements. The use of localized learning is akin to the neocortex semantic associative memory which is superior to the hippocampal form of global learning. The reason is that the hippocampal memory system rapidly learns arbitrary patterns of activity, whereas the neocortical system learns slowly. The slow learning of the neocortex is a requirement for any system that is able to eventually extract and model the similarity structure in its environment. The rapid learning of the hippocampal system, in contrast, sacrifices the ability to generalize. When both systems are intact, the hippocampal memory system trains the neocortical learning system through a process of repeated patterns, allowing for the gradual extraction of the similarity structure that is central to generalization. In FCMAC-CRI(S), its interactive relations among the selected pattern features are captured in the form of highly intuitive fuzzy IF-THEN rules, which form the knowledge base of the early warning system and provide insights into the characteristics of financial distress. The performance of the FCMAC-CRI(S) is benchmarked against that of the Cox's proportional hazard model and GenSoFNN-CRI(S) network, a functional hippocampal fuzzy semantic learning memory structure, in predicting bank failures based on a population of 3635 US banks observed over 21 years. The localized models and learning yield superior results and interpretation to fuzzy neural network such as GenSoFNN-CRI(S) that are based on global learning. The performance of the new approach as a bank failure classification and early warning system is highly encouraging.