This paper deals with a feedback control strategy for chaos suppression. The proposed strategy is an input–output control scheme which comprises an uncertainty estimator and an asymptotic linearizing feedback. The developed control scheme allows chaos suppression in spite of modeling errors and parametric variations.

Uncertainty propagation techniques achieve a more robust automatic speech recognition by modeling the information missing after speech enhancement in the short-time Fourier transform (STFT) domain in probabilistic form. This information is then propagated into the feature domain where recognition takes place and combined with observation uncertainty techniques like uncertainty decoding. In this...

In this paper, we propose a new ridge-type estimator called the new mixed ridge estimator (NMRE) by unifying the sample and prior information in linear measurement error model with additional stochastic linear restrictions. The new estimator is a generalization of the mixed estimator (ME) and ridge estimator (RE). The performances of this new estimator and mixed ridge estimator (MRE) against th...

Robust Kalman filtering problems for discrete-time Markovian jump systems with parameter and noise uncertainty were investigated. Because of the existence of stochastic Markovian switching, the covariance matrices of system state noise and observation noise are time-varying or unmeasurable instead of stationary, meanwhile the system suffers from structure parameter uncertainty as well. By view ...

`Abstract: -A fuzzy linear state estimation model is employed, which is based on Tanaka's fuzzy linear regression model, for modeling uncertainty in power system state estimation. Both measurements uncertainty as well as parametric uncertainty is considered by fuzzy estimator. The uncertain measurements and the parameters are expressed as fuzzy numbers with a triangular membership function that...

The empirical problem of natural resource management is typically the intertemporal allocation of product flows and resource stocks under uncertainty. National forest harvest scheduling is conceptualized in this study as a stochastic optimal control problem. In theory, optimal solutions to most stochastic control problems exist, but required computer costs are excessive even for problems of mod...

This paper proposes a discrete-time repetitive optimal control of electrically driven robotic manipulators using an uncertainty estimator. The proposed control method can be used for performing repetitive motion, which covers many industrial applications of robotic manipulators. This kind of control law is in the class of torque-based control in which the joint torques are generated by permanen...

Prediction of the remaining useful life (RUL) of critical components is a non-trivial task for industrial applications. RUL can differ for similar components operating under the same conditions. Working with such problem, one needs to contend with many uncertainty sources such as system, model and sensory noise. To do that, proposed models should include such uncertainties and represent the bel...

To account for the input-model and input-parameter uncertainties inherent in many simulations as well as the usual stochastic uncertainty, we present a Bayesian input-modeling technique that yields improved point and confidence-interval estimators for a selected posterior mean response. Exploiting prior information to specify the prior probabilities of the postulated input models and the associ...

In class we discussed model selection tools in the context of building a high-quality prediction model, but cautioned that statistical inference (testing, confidence intervals, etc) were unreliable following model selection. Let us review why. In week 2 we showed that our uncertainty regarding b1 as an estimator of β1 comes from the fact that if the data were to change, so would our estimate. T...