Sentence examples for admissible estimation from inspiring English sources

This means that we can approximate the set { H ~ : J ( H ~, H ) ≤ 1 / γ } of all admissible estimation errors H ~ by a (complex) ellipsoid in the parameter space [15]: D adm = { H ~ : vec H ( H ~ ) γ I adm vec ( H ~ ) ≤ 1 }.

Thus, after the relevance analysis, the number of neighbors k ̌ of the classifier could be tuned in a higher value, however, the initial estimation (no relevance) is an admissible approximation.

We aim to design a non-fragile state estimator such that, in the presence of all admissible gain variations, the estimation error converges to zero exponentially.

By the Lyapunov equation approach, their robustness is proved in the sense that for all admissible uncertainties, their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds.

By constructing a novel Lyapunov Krasovskii functional (LKF) together with free-weighting matrix technique, a new delay dependent sufficient condition is derived to estimate the neuron states through available output measurements such that, for all admissible delay bounds, the resulting estimation error system is globally asymptotically stable.

Observer performance is shown to be high with a mean absolute final carbon content estimation error for admissible heats equal to 0.0012%.

By constructing a novel Lyapunov Krasovskii functional and developing a new convex combination technique, a new delay-dependent exponential stability condition is proposed, such that for all admissible delay bounds, the resulting estimation error system is mean-square exponentially stable with a prescribed noise attenuation level in the H∞ sense.

The aim of this problem is to design an H∞ filter that ensures stochastic stability of the filtering error system and a prescribed L2-induced gain from the noise signals to the estimation error, for all admissible uncertainties.

The model estimation converged to an admissible solution.

The aim of the addressed problem is to design a state estimator such that the estimation performance is guaranteed in the mean-square sense against admissible stochastic time-delays, stochastic noises as well as stochastic fading signals.

The main purpose is to estimate the neuron states, through available output measurements, such that for all admissible time-delays, the dynamics of the estimation error is globally asymptotically stable in the mean square.