Structure identification of fuzzy model
Fuzzy Sets and Systems
Generating rules for fuzzy logic controllers by functions
Fuzzy Sets and Systems - Fuzzy information processing
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
Constructing a fuzzy controller from data
Fuzzy Sets and Systems - Special issue on methods for data analysis in classificatin and control
A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems
Automatica (Journal of IFAC)
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
An optimal T-S model for the estimation and identification of nonlinear functions
WSEAS Transactions on Systems and Control
Complex systems modeling via fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stability analysis of fuzzy control systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
General SISO Takagi-Sugeno fuzzy systems with linear rule consequent are universal approximators
IEEE Transactions on Fuzzy Systems
Reduction of fuzzy rule base via singular value decomposition
IEEE Transactions on Fuzzy Systems
Robustness design of nonlinear dynamic systems via fuzzy linear control
IEEE Transactions on Fuzzy Systems
On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A multiple Lyapunov function approach to stabilization of fuzzy control systems
IEEE Transactions on Fuzzy Systems
On relaxed LMI-based designs for fuzzy regulators and fuzzy observers
IEEE Transactions on Fuzzy Systems
Fuzzy identification using fuzzy neural networks with stable learning algorithms
IEEE Transactions on Fuzzy Systems
Effective optimization for fuzzy model predictive control
IEEE Transactions on Fuzzy Systems
Sufficient Conditions to Impose Derivative Constraints on MISO Takagi–Sugeno Fuzzy Logic Systems
IEEE Transactions on Fuzzy Systems
Interval Fuzzy Model Identification Using -Norm
IEEE Transactions on Fuzzy Systems
A Novel Fuzzy System With Dynamic Rule Base
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A min-max approach to fuzzy clustering, estimation, and identification
IEEE Transactions on Fuzzy Systems
A Survey on Analysis and Design of Model-Based Fuzzy Control Systems
IEEE Transactions on Fuzzy Systems
Adaptive Fuzzy Output Tracking Control of MIMO Nonlinear Uncertain Systems
IEEE Transactions on Fuzzy Systems
Robust H∞ Control for Uncertain Takagi–Sugeno Fuzzy Systems With Interval Time-Varying Delay
IEEE Transactions on Fuzzy Systems
Adaptive Synchronization of Uncertain Chaotic Systems Based on T–S Fuzzy Model
IEEE Transactions on Fuzzy Systems
T–S Fuzzy Bilinear Model and Fuzzy Controller Design for a Class of Nonlinear Systems
IEEE Transactions on Fuzzy Systems
Identification of Key Variables Using Fuzzy Average With Fuzzy Cluster Distribution
IEEE Transactions on Fuzzy Systems
Automatica (Journal of IFAC)
State observer design for nonlinear systems using neural network
Applied Soft Computing
Enhanced combination modeling method for combustion efficiency in coal-fired boilers
Applied Soft Computing
Expert Systems with Applications: An International Journal
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An efficient approach is presented to improve the local and global approximation and modelling capability of Takagi-Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy. The main problem is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the use of the T-S method because this type of membership function has been widely used during the last two decades in the stability, controller design and are popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S method with optimized performance in approximating nonlinear functions. A simple approach with few computational effort, based on the well known parameters' weighting method is suggested for tuning T-S parameters to improve the choice of the performance index and minimize it. A global fuzzy controller (FC) based Linear Quadratic Regulator (LQR) is proposed in order to show the effectiveness of the estimation method developed here in control applications. Illustrative examples of an inverted pendulum and Van der Pol system are chosen to evaluate the robustness and remarkable performance of the proposed method and the high accuracy obtained in approximating nonlinear and unstable systems locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity and generality of the algorithm.