Fuzzy logic, neural networks, and soft computing
Communications of the ACM
A novel single-pass thinning algorithm and an effective set of performance criteria
Pattern Recognition Letters
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
DCBAM: a discrete chainable bidirectional associative memory
Pattern Recognition Letters
Pattern Recognition Letters
BYY harmony learning, structural RPCL, and topological self-organizing on mixture models
Neural Networks - New developments in self-organizing maps
Novel Self-Organizing Takagi Sugeno Kang Fuzzy Neural Networks Based on ART-like Clustering
Neural Processing Letters
Self-Organizing Gaussian Fuzzy CMAC with Truth Value Restriction
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
Artificial Intelligence in Medicine
Neural Networks - 2005 Special issue: IJCNN 2005
Expert Systems with Applications: An International Journal
GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms
Expert Systems with Applications: An International Journal
POP-Yager: A novel self-organizing fuzzy neural network based on the Yager inference
Expert Systems with Applications: An International Journal
POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction
IEEE Transactions on Intelligent Transportation Systems
Improved MCMAC with momentum, neighborhood, and averagedtrapezoidal output
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
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying–Yang Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
A self-organizing HCMAC neural-network classifier
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach
IEEE Transactions on Neural Networks
FCMAC-Yager: A Novel Yager-Inference-Scheme-Based Fuzzy CMAC
IEEE Transactions on Neural Networks
MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections
IEEE Transactions on Neural Networks
Gaussian case-based reasoning for business failure prediction with empirical data in China
Information Sciences: an International Journal
Online FCMAC-BYY Model with Sliding Window
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Efficient advertisement discovery for audio podcast content using candidate segmentation
EURASIP Journal on Audio, Speech, and Music Processing
Hi-index | 12.05 |
Since the collapse or failure of a bank could trigger an adverse financial repercussion and generate negative impacts, it is desirable to have an early warning system (EWS) that identifies potential bank failures or high-risk banks through the traits of financial distress. This research is aimed to construct a novel fuzzy neural CMAC as an alternative to analyze bank solvency, in which a nature inspiration motivated from the famous Chinese ancient Ying-Yang philosophy is introduced to find the optimal fuzzy sets, and truth value restriction (TVR) inference scheme is employed to derive the truth-values of the rule weights. The proposed model functions as an early warning system and is able to identify the inherent traits of financial distress based on financial covariates (features) derived from publicly available financial statements. Our experiments are conducted on a benchmark dataset of a population of 3635 US banks observed over a 21 years period. Three sets of experiments are performed - bank failure classification based on the last available financial record and prediction using financial records one and two years prior to the last available financial statements. The performance of the proposed Ying-Yang FCMAC network as a bank failure classification and early warning system is very encouraging.