686 research outputs found
State estimation for discrete-time neural networks with Markov-mode-dependent lower and upper bounds on the distributed delays
Copyright @ 2012 Springer VerlagThis paper is concerned with the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters and mixed time-delays. The parameters of the neural networks under consideration switch over time subject to a Markov chain. The networks involve both the discrete-time-varying delay and the mode-dependent distributed time-delay characterized by the upper and lower boundaries dependent on the Markov chain. By constructing novel Lyapunov-Krasovskii functionals, sufficient conditions are firstly established to guarantee the exponential stability in mean square for the addressed discrete-time neural networks with Markovian jumping parameters and mixed time-delays. Then, the state estimation problem is coped with for the same neural network where the goal is to design a desired state estimator such that the estimation error approaches zero exponentially in mean square. The derived conditions for both the stability and the existence of desired estimators are expressed in the form of matrix inequalities that can be solved by the semi-definite programme method. A numerical simulation example is exploited to demonstrate the usefulness of the main results obtained.This work was supported in part by the Royal Society of the U.K., the National Natural Science Foundation of China under Grants 60774073 and 61074129, and the Natural Science Foundation of Jiangsu Province of China under Grant BK2010313
Finite-Time Boundedness of Markov Jump System with Piecewise-Constant Transition Probabilities via Dynamic Output Feedback Control
This paper first investigates the problem of finite-time boundedness of Markovian jump system with piecewise-constant transition probabilities via dynamic output feedback control, which leads to both stochastic jumps and deterministic switches. Based on stochastic Lyapunov functional, the concept of finite-time boundedness, average dwell time, and the coupling relationship among time delays, several sufficient conditions are established for finite-time boundedness and H∞ filtering finite-time boundedness. The system trajectory stays within a prescribed bound. Finally, an example is given to illustrate the efficiency of the proposed method
The History of the Quantitative Methods in Finance Conference Series. 1992-2007
This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.
Stochastic Systems: Modeling, Optimization, and Applications
The special issue of Mathematical Problems in Engineering deals with the issues of modeling, optimization, and applications associated with stochastic systems. This special issue provides a forum for researchers and practitioners to publish quality research work on modeling, optimization approaches, and their applications in the context of theory analysis and engineering developments. The accepted papers in this special issue include stochastic stability, stabilization and control optimization, stochastic optimization, particle swarm optimization, modeling and identification methods, signal processing, and robust filtering. The issue includes thirty-nine papers out of which six consider the stability and stabilization problems of stochastic systems. Twelve papers cover the problems of the controller design and relevant optimization algorithms
Dissipativity analysis for discrete time-delay fuzzy neural networks with Markovian jumps
This paper is concerned with the dissipativity analysis
and design of discrete Markovian jumping neural networks with
sector-bounded nonlinear activation functions and time-varying
delays represented by Takagi–Sugeno fuzzy model. The augmented
fuzzy neural networks with Markovian jumps are first constructed
based on estimator of Luenberger observer type. Then, applying
piecewise Lyapunov–Krasovskii functional approach and stochastic
analysis technique, a sufficient condition is provided to guarantee
that the augmented fuzzy jump neural networks are stochastically
dissipative. Moreover, a less conservative criterion is established
to solve the dissipative state estimation problem by using
matrix decomposition approach. Furthermore, to reduce the computational
complexity of the algorithm, a dissipative estimator is
designed to ensure stochastic dissipativity of the error fuzzy jump
neural networks. As a special case, we have also considered the
mixed H∞ and passive analysis of fuzzy jump neural networks.
All criteria can be formulated in terms of linear matrix inequalities.
Finally, two examples are given to show the effectiveness and
potential of the new design techniques.Yingqi Zhang, Peng Shi, Ramesh K. Agarwal, and Yan Sh
State Estimation for Time-Delay Systems with Markov Jump Parameters and Missing Measurements
This paper is concerned with the state estimation problem for a class of time-delay systems with Markovian jump parameters and missing measurements, considering the fact that data missing may occur in the process of transmission and its failure rates are governed by random variables satisfying certain probabilistic distribution. By employing a new Lyapunov function and using the convexity property of the matrix inequality, a sufficient condition for the existence of the desired state estimator for Markovian jump systems with missing measurements can be achieved by solving some linear matrix inequalities, which can be easily facilitated by using the standard numerical software. Furthermore, the gain of state estimator can also be derived based on the known conditions. Finally, a numerical example is exploited to demonstrate the effectiveness of the proposed method
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