期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 28, 期 9, 页码 2101-2114出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2016.2573853
关键词
Filtering; neural networks (NNs); semi-Markovian jump systems (S-MJSs); time delay
类别
资金
- Australian Research Council [DP140102180, LP140100471]
- National Natural Science Foundation of China [61573112, 61525303, U1509217]
- Heilongjiang Outstanding Youth Science Fund [JC201406]
- Fok Ying Tung Education Foundation [141059]
- Self-Planned Task of State Key Laboratory of Robotics and System (HIT) [201505B]
- Top-Notch Young Talents Program of China
This paper investigates the problem of exponential passive filtering for a class of stochastic neutral-type neural networks with both semi-Markovian jump parameters and mixed time delays. Our aim is to estimate the states by designing a Luenberger-type observer, such that the filter error dynamics are mean-square exponentially stable with an expected decay rate and an attenuation level. Sufficient conditions for the existence of passive filters are obtained, and a convex optimization algorithm for the filter design is given. In addition, a cone complementarity linearization procedure is employed to cast the nonconvex feasibility problem into a sequential minimization problem, which can be readily solved by the existing optimization techniques. Numerical examples are given to demonstrate the effectiveness of the proposed techniques.
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