Construction of fuzzy inference rules by NDF and NDFL
International Journal of Approximate Reasoning - Special issue on fuzzy logic and neural networks for pattern recognition and control
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
A survey of fuzzy clustering algorithms for pattern recognition. I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
GenSoFNN: a generic self-organizing fuzzy neural network
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
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Fuzzy neural network structure identification based on soft competitive learning
International Journal of Hybrid Intelligent Systems
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
A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling
Expert Systems with Applications: An International Journal
Stock trading with cycles: A financial application of ANFIS and reinforcement learning
Expert Systems with Applications: An International Journal
Cultural dependency analysis for understanding speech emotion
Expert Systems with Applications: An International Journal
A novel brain-inspired neuro-fuzzy hybrid system for artificial ventilation modeling
Expert Systems with Applications: An International Journal
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The existing Self-Organizing Takagi Sugeno Kang Fuzzy Neural Networks (S-TSKfnn) structure uses virus infection clustering (VIC) method to generate fuzzy rules. In this paper, we propose a novel architecture called Modified S-TSKfnn (MS-TSKfnn) that uses ART-like clustering called discrete incremental clustering (DIC). By doing so, MS-TSKfnn is able to handle online data input, and its performance is also enhanced. Most importantly, the accurate clustering in the fuzzy set derivation has significantly reduced the number of fuzzy TSK rules necessary to describe a problem. Extensive simulations are conducted using MS-TSKfnn and its performance is encouraging when benchmarked against other established neuro-fuzzy systems. The empirical work also firmly demonstrated the importance of clustering within a fuzzy neural reasoning system in ensuring a compact and expressive fuzzy rate base.