Adaptive signal processing: theory and applications
Adaptive signal processing: theory and applications
Structure identification of fuzzy model
Fuzzy Sets and Systems
The Cauchy problem for fuzzy differential equations
Fuzzy Sets and Systems
Applications of type-2 fuzzy logic systems to forecasting of time-series
Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy Sets and Systems
Neuro-Control Systems: Theory and Applications
Neuro-Control Systems: Theory and Applications
Control and identification of non-linear systems affected by noise using wavelet network
Second international workshop on Intelligent systems design and application
Choquet fuzzy integral based modeling of nonlinear system
Applied Soft Computing
New fuzzy wavelet neural networks for system identification and control
Applied Soft Computing
Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Fuzzy Systems
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters
IEEE Transactions on Fuzzy Systems
MPEG VBR video traffic modeling and classification using fuzzy technique
IEEE Transactions on Fuzzy Systems
Fuzzy wavelet networks for function learning
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Structure identification of generalized adaptive neuro-fuzzy inference systems
IEEE Transactions on Fuzzy Systems
Computing derivatives in interval type-2 fuzzy logic systems
IEEE Transactions on Fuzzy Systems
A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
LMS learning algorithms: misconceptions and new results on converence
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Design of interval networks based on neural network and Choquet integral
Applied Soft Computing
Computers & Mathematics with Applications
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
| Hi-index | 0.00 |
We propose a novel method for the identification of non-linear system by utilizing some of the important properties of wavelets like denoising, compression, multiresolution along with the concepts of fuzzy logic. Two new type-2 fuzzy wavelet networks (T2FWNs) are proposed here. These T2FWNs can handle rule uncertainties in a better way because of using the type-2 fuzzy sets in modeling and fuzzy differential (FD) and Lyapunov stability during learning. Lot of work has been done in the identification of non-linear system by using the models based on type-1 fuzzy logic system (FLS). But in practice they are unable to handle uncertainties in the rules. The robustness of the system is assured by Lyapunov stability (LS). Also we have explored the properties of wavelets and FLS to handle the uncertainties efficiently. As the stability of the model is highly dependent on the learning of the system we use Lyapunov stability in combination with fuzzy differential. FD gives the range of variation of parameters having lower and upper bound in which the system is stable. The performance of T2FWN is compared with type-1 FLS, FWN [D.W.C. Ho, P.-A. Zhang, J. Xu, Fuzzy wavelet networks for function learning, IEEE Trans. Fuzzy Syst. 9 (February (1)) 2000] and FWNN [S. Srivastava, M. Singh, M. Hanmandlu, A.N. Jha, New fuzzy wavelet neural networks for system identification and control, Intl. J. Appl. Soft Comput. 6 (November (I)) 2005, 1-17]. It is shown that noise and disturbance in the reference signal are reduced with wavelets. A comparison of three learning algorithms: (i) gradient descent (GD) (ii) a combination of Lyapunov stability and fuzzy differential (LSFD) and, (iii) a combination of (i) and (ii) is done.