This paper considers the problem of delay-dependent stabilization criterion for uncertain distributed systems with time-varying delay. The time-varying delay considered is assumed to belong to a given interval in whic...
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In this paper, we will investigate a class of functional system whose coefficient satisfies the local Lipschitz condition and the one-sided polynomial growth condition under Markovian switching. We introduce two appro...
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In this paper, for a class of switched stochastic nonlinear systems with time-varying delays, the output feedback stabilization problem is addressed based on single hidden layer feed-forward network (SLFN) and backste...
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This paper describes development of the module, which analyzes medical images and its use is also possible in sectors other than medicine. A software module can base on events captured on the ECG graph store images, w...
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This paper describes the processing of photographs in the newly created module for 2D modeling, which will be integrated into the system FOTOM NG. This is a usable application in medicine to analyze images of probes a...
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In this paper, we address the problem of multitarget tracking with unknown measurement noise variance parameters by the probability hypothesis density (PHD) filter. Based on the concept of conjugate prior distribution...
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ISBN:
(纸本)9781467357159
In this paper, we address the problem of multitarget tracking with unknown measurement noise variance parameters by the probability hypothesis density (PHD) filter. Based on the concept of conjugate prior distributions for noise statistics, the inverse-Gamma distributions are employed to describe the dynamics of the noise variance parameters and a novel implementation to the PHD recursion is developed by representing the predicted and the posterior intensities as mixtures of Gaussian-inverse-Gamma terms. As the target state and the noise variance parameters are coupled in the likelihood functions, the variational Bayesian approximation approach is applied so that the posterior is derived in the same form as the prior and the resulting algorithm is recursive. A numerical example is provided to illustrate the effectiveness of the proposed filter.
This paper studies the problem of state estimation for jump Markov linear systems with uncompensated biases. By describing the state and the measurement biases as additive random variables, a suboptimal filter has bee...
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ISBN:
(纸本)9781479901777
This paper studies the problem of state estimation for jump Markov linear systems with uncompensated biases. By describing the state and the measurement biases as additive random variables, a suboptimal filter has been developed by applying the basic interacting multiple model (IMM) approach. To derive a precise representation of the biases contributions to the state estimation, three auxiliary matrices are introduced with respect to the correlation between the state estimation errors and the biases, which helps to derive mode-conditioned estimates in the framework of the IMM. A numerical example involving tracking a maneuvering target is provided to compare the performance of the proposed filter with that of the augmented state filter.
This paper is devoted to the finite-time control problem for multiple manipulators, where unmodeled dynamics is taken into consideration. An effective coordinated control strategy is introduced, under which distribute...
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ISBN:
(纸本)9781479901777
This paper is devoted to the finite-time control problem for multiple manipulators, where unmodeled dynamics is taken into consideration. An effective coordinated control strategy is introduced, under which distributed protocols with continuous feedbacks are proposed. By applying the homogeneous theory for stability analysis, it is proven that the multi-robot system can be globally finite-time stabilized through our protocols. Numerical simulations on four manipulators with two degrees of freedom are presented to validate the effectiveness of the control strategy.
This note proposes a method to estimate the unknown switching topology of complex dynamical networks in finite time on the premise of persistence of excitation (PE). The estimation results are independent of estimator...
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ISBN:
(纸本)9781849195386
This note proposes a method to estimate the unknown switching topology of complex dynamical networks in finite time on the premise of persistence of excitation (PE). The estimation results are independent of estimator we choose. When PE condition can't be satisfied, an adaptive predictor is applied to estimate topology matrix. A sufficient condition ensuring the asymptotic convergence of the estimation error towards zero is given. Finally, the validity and feasibility of our methods are illustrated through a numerical example.
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