Najmeh Daroogheh

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In this paper, a novel method for a time-varying parameter estimation technique using particle filters is proposed based on the concept of Recursive Prediction Error (RPE). According to the proposed method, a parallel structure for both state and parameter estimation in a nonlinear non-Gaussian system is developed. The performance of the developed framework(More)
In this paper, a novel hybrid structure is proposed for the development of health monitoring techniques of nonlinear systems by integration of model-based and computationally intelligent and data-driven techniques. In our proposed health monitoring framework, the well-known particle filtering method is utilized to estimate the states as well as the health(More)
In this paper, we propose and develop a methodology for nonlinear systems health monitoring by modeling the damage and degradation mechanism dynamics as ”slow” states that are augmented with the system ”fast” dynamical states. This augmentation results in a two-time scale nonlinear system that is utilized for development of health estimation and prediction(More)
In this paper, a dual estimation methodology is developed for both time-varying parameters and states of a nonlinear stochastic system based on the Particle Filtering (PF) scheme. Our developed methodology is based on a concurrent implementation of state and parameter estimation filters as opposed to using a single filter for simultaneously estimating the(More)
In this paper a general framework is developed for state estimation in a class of nonlinear continuous-time singularly perturbed systems. Our approach is based on the hybrid extended Kalman filter in which observations are originated from discrete measurements. The developed framework is also extended to include linearization error in the observation(More)
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