In this work, the fault estimation is formulated as a parameter estimation problem.
Essentially, tensor imaging can be cast like any other MIMO estimation problem, formulated as either a Bayesian or classical estimation minimum variance unbiased estimator (MVUE) problem.
First, a parameter estimation technique, based on an uncertainty set estimation, is formulated.
For models with more reactions than measured species, a common scenario in biological modeling, the parameter estimation is formulated as a nested optimization problem based on incremental parameter estimation strategy.
The reliability estimation is formulated in the load process space.
The moment method of estimation is formulated by equating the population and sample PWMs.
The design problem of the distributed control and estimation is formulated with bilinear matrix inequalities in an augmented state space.
A novel sliding surface based on the states and disturbance estimation is formulated as: rho = x_{2} + kx_{1} + hat{d} (12 where k > 0 is the parameter to be designed.
Compared to our previous work, the improved stochastic model for location estimation is formulated in a discretized graph-based representation of the indoor environment.
Conventional ML estimation is formulated in such a way that the unknown parameter is a multivariate vector, i.e., θ = [a0... aL-1τ0... τL-1] T. When the number of paths L is large, the computational complexity becomes prohibitive.
