Introduction to non-linear optimization
Introduction to non-linear optimization
A course in fuzzy systems and control
A course in fuzzy systems and control
On the solution of differential equations with fuzzy spline wavelets
Fuzzy Sets and Systems
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
On generating FC3 fuzzy rule systems from data usingevolution strategies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
NeuroFAST: on-line neuro-fuzzy ART-based structure and parameterlearning TSK model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Fuzzy wavelet networks for function learning
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
Unification of neural and wavelet networks and fuzzy systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Nonlinear system identification via Laguerre network based fuzzy systems
Fuzzy Sets and Systems
Fuzzy wavelet neural network models for prediction and identification of dynamical systems
IEEE Transactions on Neural Networks
Computers & Mathematics with Applications
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In this paper, we propose simple but effective two different fuzzy wavelet networks (FWNs) for system identification. The FWNs combine the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and discrete wavelet transforms (DWT). The proposed FWNs consist of a set of if-then rules and, then parts are series expansion in terms of wavelets functions. In the first system, while the only one scale parameter is changing with it corresponding rule number, translation parameter sets are fixed in each rule. As for the second system, DWT is used completely by using wavelet frames. The performance of proposed fuzzy models is illustrated by examples and compared with previously published examples. Simulation results indicate the remarkable capabilities of the proposed methods. It is worth noting that the second FWN achieves high function approximation accuracy and fast convergence.