ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Fuzzy solutions to partial differential equations: adaptive approach
IEEE Transactions on Fuzzy Systems
Fuzzy filter design for itô stochastic systems with application to sensor fault detection
IEEE Transactions on Fuzzy Systems
Robust H∞ reliable fuzzy control for Markovian jump nonlinear singular systems
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
Filtering for discrete fuzzy stochastic systems with sensor nonlinearities
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Signal Processing
IEEE Transactions on Fuzzy Systems
Robust H∞ filter design for time-delay systems with saturation
International Journal of Automation and Computing
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This paper describes the robust Hinfin fuzzy filtering design for a class of nonlinear stochastic systems. The system dynamic is modelled by Itocirc-type stochastic differential equations. For general nonlinear stochastic systems, the Hinfin filter can be obtained by solving a second-order nonlinear Hamilton-Jacobi inequality. In general, it is difficult to solve the second-order nonlinear Hamilton-Jacobi inequality. In this paper, using fuzzy approach [Takagi-Sugeno (T-S) fuzzy model], the Hinfin fuzzy filtering design for the nonlinear stochastic systems can be given via solving linear matrix inequalities (LMIs) instead of a second-order Hamilton-Jacobi inequality. When the worst-case fuzzy disturbance is considered, a near minimum variance fuzzy filtering problem is also solved by minimizing the upper bound on the variance of the estimation error. The near minimum variance fuzzy filtering problem under the worst-case fuzzy disturbance is also characterized in terms of linear matrix inequality problem (LMIP), which can be efficiently solved by the convex optimization techniques. Simulation examples are provided to illustrate the design procedure and expected performance