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Multithreshold Entropy Linear Classifier (MELC) is a density based model which searches for a linear projection maximizing the Cauchy-Schwarz Divergence of dataset kernel density estimation. Despite its good empirical results, one of its drawbacks is the optimization speed. In this paper we analyze how one can speed it up through solving an approximate problem. We analyze two methods, both similar...
The paper deals with the problem of robust fault estimation of non-linear discrete-time systems. In particular, it is shown how to employ the unknown input observer approach and the H∞ strategy to design a robust fault estimation filter. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the fault estimation error while guaranteeing...
The paper deals with the problem of estimating an unknown input distribution matrix for non-linear discrete-time stochastic systems. In particular, it is shown how to use the unscented Kalman filter as an unknown input filter. Subsequently, an analysis of the impact of unknown input decoupling on the fault detection is performed and a suitable fault detection condition is developed. Based on the achieved...
The paper deals with the problem of designing an unknown input filter for non-linear discrete-time stochastic systems. In particular, it is shown how to design an unknown input filter for a single (constant) unknown input distribution matrix. Subsequently, the interacting multiple model algorithm is employed to tackle the problem of selecting an appropriate unknown input distribution matrix from a...
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