Two new operators in rough set theory with applications to fuzzy sets
Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy Hyperbolic Neural Network Model and Its Application in H ∞Filter Design
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
International Journal of Intelligent Systems Technologies and Applications
International Journal of Computer Applications in Technology
Identification of neurofuzzy models using GTLS parameter estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
H∞ control for fuzzy singularly perturbed systems
Fuzzy Sets and Systems
Nonlinear internal model control based on transformed fuzzy hyperbolic model
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Fuzzy hyperbolic neural network with time-varying delays
Fuzzy Sets and Systems
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
GFHM model and control for uncertain chaotic system
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
H∞ quantized control for nonlinear networked control systems
Fuzzy Sets and Systems
A new fuzzy identification method based on adaptive critic designs
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
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In this paper, we propose a new fuzzy hyperbolic model for a class of complex systems, which is difficult to model. The fuzzy hyperbolic model is a nonlinear model in nature and can be easily derived from a set of fuzzy rules. It can also be seen as a feedforward neural network model and so we can identify the model parameters by BP-algorithm. We prove that the stable controller can be designed based on linear system theory. Two methods of designing the controller for the fuzzy hyperbolic model are proposed. The results of simulation support the effectiveness of the model and the control scheme