Combining uncertainty and imprecision in models of medical diagnosis
Information Sciences: an International Journal
Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons
Information Sciences: an International Journal
NeuroFAST: on-line neuro-fuzzy ART-based structure and parameterlearning TSK model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
Fuzzy identification using fuzzy neural networks with stable learning algorithms
IEEE Transactions on Fuzzy Systems
TSK-fuzzy modeling based on ϵ-insensitive learning
IEEE Transactions on Fuzzy Systems
Combination of online clustering and Q-value based GA for reinforcement fuzzy system design
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Some new results on system identification with dynamic neural networks
IEEE Transactions on Neural Networks
Neural-network construction and selection in nonlinear modeling
IEEE Transactions on Neural Networks
A soft computing method for detecting lifetime building thermal insulation failures
Integrated Computer-Aided Engineering
| Hi-index | 12.05 |
This paper describes a novel nonlinear modeling approach with fuzzy rules and support vector machines. Structure identification is realized by an online clustering method and fuzzy support vector machines, the fuzzy rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. The modeling errors are proven to be robustly stable with bounded uncertainties by a Lyapunov method and an input-to-state stability technique. Comparisons with other related works are made through an application of gas furnace process. The results demonstrate that our approach has good accuracy, and this method is suitable for online fuzzy modeling.