The notion of the extended dissipativity based state estimation methodology is launched to investigate the L2−L∞ state estimation, H∞ state estimation, passivity state estimation, mixed H∞ and passivity state estimation, and (Q,S,R −γ-dissipativity state estimation of SNNs by choosing free weighting matrices.
However, those filters are intended for linear models, and the success of the MCC-based state estimation in a nonlinear continuous-time stochastic environment, which often underlies radar tracking modeling, is still debatable.
The method is based on training data that is analyzed offline via Singular Value Decomposition to determine clustering vectors that can be utilized for state estimation in real-time.
Ogawa, K., Inui, T. & Sugio, T. Neural correlates of state estimation in visually guided movements: an event-related fMRI study.
In this paper, we will analysis this problem based on nonlinear state estimation model which is more practical to the actual system operation.
Possible faults in the system are chosen and strategies for detection may be developed, to identify and to diagnose them, based on the state estimation.
Zhao et al. in [23] and [24] proposed a forecasting-aided implementation method to detect FDIA based on AC state estimation model.
This paper proposes an alternative methodology based on the Singular Value Decomposition SVDD) to evaluate the Transient State Estimation (TSE) in the time domain of a power system.
