Fuzzy systems theory and its applications
Fuzzy systems theory and its applications
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
On-line optimization of fuzzy systems
Information Sciences: an International Journal
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
Indefinite-quadratic estimation and control: a unified approach to H2 and H∞ theories
Indefinite-quadratic estimation and control: a unified approach to H2 and H∞ theories
Sum normal optimization of fuzzy membership functions
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Training fuzzy systems with the extended Kalman filter
Fuzzy Sets and Systems - Fuzzy systems
Game theory approach to discrete H∞ filter design
IEEE Transactions on Signal Processing
A game theory approach to constrained minimax state estimation
IEEE Transactions on Signal Processing
Generating optimal adaptive fuzzy-neural models of dynamicalsystems with applications to control
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A new method for constructing membership functions and fuzzy rulesfrom training examples
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Design of fuzzy controllers with adaptive rule insertion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Reduction of fuzzy rule base via singular value decomposition
IEEE Transactions on Fuzzy Systems
Fuzzy logic for digital phase-locked loop filter design
IEEE Transactions on Fuzzy Systems
Design of fuzzy systems using neurofuzzy networks
IEEE Transactions on Neural Networks
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
IEEE Transactions on Neural Networks
Compromise ratio method for fuzzy multi-attribute group decision making
Applied Soft Computing
Optimization of Fuzzy Membership Function Using Clonal Selection
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Intrusion detection using fuzzy association rules
Applied Soft Computing
ACS'08 Proceedings of the 8th conference on Applied computer scince
Classifying 3D Human Motions by Mixing Fuzzy Gaussian Inference with Genetic Programming
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Optimization of multiple input-output fuzzy membership functions using clonal selection algorithm
Expert Systems with Applications: An International Journal
Recognizing 3D human motions using fuzzy quantile inference
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
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Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a specific shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a few variables and the membership optimization problem can be reduced to a parameter optimization problem. The parameter optimization problem can then be formulated as a nonlinear filtering problem. In this paper we solve the nonlinear filtering problem using H"~ state estimation theory. However, the membership functions that result from this approach are not (in general) sum normal. That is, the membership function values do not add up to one at each point in the domain. We therefore modify the H"~ filter with the addition of state constraints so that the resulting membership functions are sum normal. Sum normality may be desirable not only for its intuitive appeal but also for computational reasons in the real time implementation of fuzzy logic systems. The methods proposed in this paper are illustrated on a fuzzy automotive cruise controller and compared to Kalman filtering based optimization.