A new recurrent neurofuzzy network for identification of dynamic systems
Fuzzy Sets and Systems
An improved fuzzy neural network based on T-S model
Expert Systems with Applications: An International Journal
Design of robust fuzzy neural network controller with reduced rule base
International Journal of Hybrid Intelligent Systems
System identification using hierarchical fuzzy neural networks with stable learning algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
A Fast Fuzzy Neural Modelling Method for Nonlinear Dynamic Systems
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
The clustering algorithm for nonlinear system identification
WSEAS Transactions on Computers
An optimal T-S model for the estimation and identification of nonlinear functions
WSEAS Transactions on Systems and Control
Robust neural-fuzzy method for function approximation
Expert Systems with Applications: An International Journal
Online fuzzy modeling with structure and parameter learning
Expert Systems with Applications: An International Journal
Clustering for nonlinear system identification
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
New optimal approach for the identification of Takagi-Sugeno fuzzy model
CONTROL'08 Proceedings of the 4th WSEAS/IASME international conference on Dynamical systems and control
Realization of XOR by SIRMs Connected Fuzzy Inference Method
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Identification of neurofuzzy models using GTLS parameter estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the estimation of parameters of Takagi-Sugeno fuzzy filte
IEEE Transactions on Fuzzy Systems
Adaptive fuzzy filtering in a deterministic setting
IEEE Transactions on Fuzzy Systems
Identification of a class of nonlinear systems by a continuous-time recurrent neurofuzzy network
ACC'09 Proceedings of the 2009 conference on American Control Conference
Fuzzy Sets and Systems
SOFMLS: online self-organizing fuzzy modified least-squares network
IEEE Transactions on Fuzzy Systems
A modified gradient-based neuro-fuzzy learning algorithm and its convergence
Information Sciences: an International Journal
Black-box identification of a class of nonlinear systems by a recurrent neurofuzzy network
IEEE Transactions on Neural Networks
Multiple model iterative learning control
Neurocomputing
Variational bayes for a mixed stochastic/deterministic fuzzy filter
IEEE Transactions on Fuzzy Systems
On maximum likelihood fuzzy neural networks
Fuzzy Sets and Systems
A Generalized Ellipsoidal Basis Function Based Online Self-constructing Fuzzy Neural Network
Neural Processing Letters
A new recurrent neurofuzzy network for identification of dynamic systems
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Nonlinear function approximation using fuzzy functional SIRMs inference model
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
An energy-gain bounding approach to robust fuzzy identification
Automatica (Journal of IFAC)
SIRMs connected fuzzy inference method adopting emphasis and suppression
Fuzzy Sets and Systems
An information theoretic sparse kernel algorithm for online learning
Expert Systems with Applications: An International Journal
On-line modeling via fuzzy support vector machines and neural networks
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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In general, fuzzy neural networks cannot match nonlinear systems exactly. Unmodeled dynamic leads parameters drift and even instability problem. According to system identification theory, robust modification terms must be included in order to guarantee Lyapunov stability. This paper suggests new learning laws for Mamdani and Takagi-Sugeno-Kang type fuzzy neural networks based on input-to-state stability approach. The new learning schemes employ a time-varying learning rate that is determined from input-output data and model structure. Stable learning algorithms for the premise and the consequence parts of fuzzy rules are proposed. The calculation of the learning rate does not need any prior information such as estimation of the modeling error bounds. This offer an advantage compared to other techniques using robust modification.