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|Title:||Neural network-based passive filtering for delayed neutral-type semi-Markovian jump systems|
|Citation:||IEEE Transactions on Neural Networks and Learning Systems, 2017; 28(9):2101-2114|
|Peng Shi, Fanbiao Li, Ligang Wu and Cheng-Chew Lim|
|Abstract:||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.|
|Keywords:||Filtering; neural networks (NNs); semi-Markovian jump systems (S-MJSs); time delay|
|Rights:||© 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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