DocumentCode :
1384108
Title :
Bounded H_{\\infty } Synchronization and State Estimation for Discrete Time-Varying Stochastic Complex Networks Over a Finite Horizon
Author :
Shen, Bo ; Wang, Zidong ; Liu, Xiaohui
Author_Institution :
Sch. of Inf. Sci. & Technol., Donghua Univ., Shanghai, China
Volume :
22
Issue :
1
fYear :
2011
Firstpage :
145
Lastpage :
157
Abstract :
In this paper, new synchronization and state estimation problems are considered for an array of coupled discrete time-varying stochastic complex networks over a finite horizon. A novel concept of bounded H synchronization is proposed to handle the time-varying nature of the complex networks. Such a concept captures the transient behavior of the time-varying complex network over a finite horizon, where the degree of bounded synchronization is quantified in terms of the H-norm. A general sector-like nonlinear function is employed to describe the nonlinearities existing in the network. By utilizing a timevarying real-valued function and the Kronecker product, criteria are established that ensure the bounded H synchronization in terms of a set of recursive linear matrix inequalities (RLMIs), where the RLMIs can be computed recursively by employing available MATLAB toolboxes. The bounded H state estimation problem is then studied for the same complex network, where the purpose is to design a state estimator to estimate the network states through available output measurements such that, over a finite horizon, the dynamics of the estimation error is guaranteed to be bounded with a given disturbance attenuation level. Again, an RLMI approach is developed for the state estimation problem. Finally, two simulation examples are exploited to show the effectiveness of the results derived in this paper.
Keywords :
H control; complex networks; control nonlinearities; discrete time systems; linear matrix inequalities; recursive estimation; state estimation; stochastic systems; synchronisation; time-varying networks; Kronecker product; MATLAB; bounded H synchronization; discrete time varying stochastic complex network; disturbance attenuation level; estimation error; finite horizon; recursive linear matrix inequality; state estimation; Attenuation; Complex networks; State estimation; Stochastic processes; Symmetric matrices; Synchronization; Transient analysis; Bounded ${H_{infty}}$ synchronization; complex networks; discrete-time networks; finite horizon; recursive linear matrix inequalities; stochastic networks; time-varying networks; transient behavior; Artificial Intelligence; Computer Simulation; Cortical Synchronization; Neural Networks (Computer); Nonlinear Dynamics; Software Design; Stochastic Processes; Time Factors;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2010.2090669
Filename :
5640676
Link To Document :
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