A self-generating method for fuzzy system design
Fuzzy Sets and Systems
Industrial Applications of Fuzzy Logic and Intelligent Systems
Industrial Applications of Fuzzy Logic and Intelligent Systems
Survey of Intelligent Control Techniques for Humanoid Robots
Journal of Intelligent and Robotic Systems
Design and Analysis of Experiments
Design and Analysis of Experiments
Application of evolving Takagi-Sugeno fuzzy model to nonlinear system identification
Applied Soft Computing
Expert Systems with Applications: An International Journal
A New Approach For TSK-Type Fuzzy Model Design
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Complex systems modeling via fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Tuning of a neuro-fuzzy controller by genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A TSK-type neurofuzzy network approach to system modeling problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning
IEEE Transactions on Fuzzy Systems
Hybrid fuzzy control of robotics systems
IEEE Transactions on Fuzzy Systems
A Survey on Analysis and Design of Model-Based Fuzzy Control Systems
IEEE Transactions on Fuzzy Systems
Modeling, stability and control of biped robots-a general framework
Automatica (Journal of IFAC)
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
Stability analysis and robustness design of nonlinear systems: An NN-based approach
Applied Soft Computing
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This paper introduces a simple, systematic and effective method for designing Takagi-Sugeno (T-S) fuzzy systems utilizing a significantly smaller training data set versus existing methods. Creating proper training data is usually not an easy task and requires spending considerable time and resources. The proposed method first uses a three-level factorial design to partition the output space. Next the least square technique is used to estimate each of the partitioned output spaces. The membership functions are introduced with only three variables (min, max and number of membership functions). Fuzzy rules are generated with respect to the partitioned output surfaces and the membership functions. The proposed method is applied to two benchmark problems, controlling an inverted pendulum as well as modeling a nonlinear function. In the case of the inverted pendulum simulation results demonstrate significant improvement. In the case of nonlinear function modeling we demonstrated sufficient accuracy with only 9 training data, which represents 98% reduction in the number of training data compare to other method. Additionally, the proposed method offers extremely low computation time allowing it to be used with adaptive type systems.