Adaptive signal processing
Structure identification of fuzzy model
Fuzzy Sets and Systems
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Uncertainty, fuzzy logic, and signal processing
Signal Processing - Special issue on fuzzy logic in signal processing
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
IEEE Transactions on Information Technology in Biomedicine
Avoiding exponential parameter growth in fuzzy systems
IEEE Transactions on Fuzzy Systems
The shape of fuzzy sets in adaptive function approximation
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Applied Soft Computing
A survey on use of soft computing methods in medicine
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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In this paper, we apply a fast training paradigm to the optimization of fuzzy approximator and a nonlinear adaptive fuzzy approximator (NAFA) is constructed. Using TSK fuzzy rules, the structured knowledge along with numerical information are parameterized and utilized in the NAFA, which can be easily configured as a multi-layer network when its transparency is desired. We propose a fast training paradigm, which is actually a combination of Kalman filtering and LMS adaptation, to optimize the linear and nonlinear parameters of the NAFA separately. The NAFA is characterized by concise representation of structured knowledge, fast learning capability, as well as universal approximation property. The NAFA is applied to forecast the non-stationary EEG time-series and to estimate single-sweep evoked potentials (EPs). The corresponding simulation results are given. It is concluded that the NAFA technique can provide efficient nonlinear separation of single-sweep EPs, which allows for quantitative examination of the cross-trial variability of clinical EPs.