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This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM(More)
The paper studies the problem of simultaneously estimating the state and the fault of linear stochastic discrete-time varying systems with unknown inputs. The fault and the unknown inputs affect both the system state and output. However, if the dynamical evolution models of the fault and the unknown inputs are available the filtering problem is solved by(More)
This paper presents a method to design Sliding Mode Observers (SMO) for a class of linear system using Linear Matrix Inequalities (LMIs). The switching surface is set to be the difference between the observer and system output. In terms of LMIs, a necessary and sufficient condition is derived for the existence of a sliding-mode observer guaranteeing a(More)
This paper presents a new robust filter structure to solve the simultaneous state and fault estimation problem of linear stochastic discrete-time systems with unknown disturbances. The method is based on the assumption that the fault and the unknown disturbances affect both the system state and the output, and no prior knowledge about their dynamical(More)
This paper studies recursive optimal filtering as well as robust fault and state estimation for linear stochastic systems with unknown disturbances. It proposes a new recursive optimal filter structure with transformation of the original system. This transformation is based on the singular value decomposition of the direct feedthrough matrix distribution of(More)
— This paper presents a new robust fault and state estimation based on recursive least square filter for linear stochastic systems with unknown disturbances. The novel elements of the algorithm are : a simple, easily implementable, square root method which is shown to solve the numerical problems affecting the unknown input filter algorithm and related(More)
Problem statement: Fault reconstruction scheme is different from the majority of Fault Detection and Isolation (FDI) methods described in the literature in the sense that it not only detects and isolates the fault, but provides an estimate of the fault. This approach is very useful for incipient faults and slow drifts, which are very difficult to detect.(More)
In this paper, we are interested in the parametric identification of the Wiener model. In this model, the used memory-less nonlinearity is the saturation with hysteresis. Equation is obtained by the decomposition technique and the superposition of the two switched saturation preposition is leading to pseudo linear regression. In this identification problem,(More)