ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
New results on H∞ filtering for fuzzy time-delay systems
IEEE Transactions on Fuzzy Systems
Delay-dependent H∞ and generalized H2 filtering for delayed neural networks
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements
IEEE Transactions on Signal Processing
A delay-dependent approach to robust H∞ filtering for uncertain discrete-time state-delayed systems
IEEE Transactions on Signal Processing
Robust Fuzzy Filter Design for a Class of Nonlinear Stochastic Systems
IEEE Transactions on Fuzzy Systems
Fuzzy H∞ Filter Design for a Class of Nonlinear Discrete-Time Systems With Multiple Time Delays
IEEE Transactions on Fuzzy Systems
Delayed Standard Neural Network Models for Control Systems
IEEE Transactions on Neural Networks
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This paper is concerned with multi-sensor optimal H"~ fusion filtering for a class of nonlinear intelligent systems with time delays. A unified model consisting of a linear dynamic system and a bounded static nonlinear operator is employed to describe these systems, such as neural networks and Takagi and Sugeno (T-S) fuzzy models. Based on the H"~ performance analysis of this unified model using the linear matrix inequality (LMI) approach, centralized and distributed fusion filters are designed for multi-sensor time-delayed systems to guarantee the asymptotic stability of the fusion error systems and to reduce the influence of noise on the filtering error. The parameters of these filters are obtained by solving the eigenvalue problem (EVP). As most artificial neural networks or fuzzy systems with or without time delays can be described with this unified model, fusion filter design for these systems can be done in a unified way. Simulation examples are provided to illustrate the design procedure and effectiveness of the proposed approach.