numerical solutions obtained by the meshless local petrov–galerkin (mlpg) method are presented for two-dimensional steady-state heat conduction problems. the mlpg method is a truly meshless approach, and neither the nodal connectivity nor the background mesh is required for solving the initial-boundary-value problem. the penalty method is adopted to efficiently enforce the essential boundary co...

In this paper, we present the steady state analysis of adaptive IIR notch filters based on the least mean -power error criterion. We consider the cases when the sinusoidal signal is contaminated with white Gaussian noise and . We first derive two difference equations for the convergence of the mean and the Mean Square Error (MSE) of the adaptive filter’s notch coefficient, and then give the ste...

A new technique for the latent state estimation of a wide class of nonlinear time series models is proposed. In particular, we develop a partially linearized sigma point filter in which random samples of possible state values are generated at the prediction step using an exact moment matching algorithm and then a linear programming-based procedure is used in the update step of the state estimat...

Accurate alignment and vertical channel instability play an important role in the strap-down inertial navigation system (SINS), especially in the case that precise navigation has to be achieved over long periods of time. Due to poor initialization as well as the cumulative errors of low-cost inertial measurement units (IMUs), initial alignment is not sufficient to achieve required navigation ac...

In this paper, a novel interactive multiple model particle filter (IMMPF) is developed after a Bayesian estimator for maneuvering target tracking in clutter is derived theoretically. In this new algorithm, base state estimation and modal state estimation are completely separated to control the number of particles in each maneuvering mode. Only continuous-valued particles are used to numerically...

In most chemical processes only some measurements are available online while other measurements are available infrequently and often with long delays. Multirate state estimation can optimally combine these different classes of measurements to improve the estimation quality compared to the fast measurements alone. The nature of measurements at different sampling intervals which are subject to de...

Consider a stochastic frontier model with one-sided inefficiency u, and suppose that the scale of u depends on some variables (firm characteristics) z. A “one-step” model specifies both the stochastic frontier and the way in which u depends on z, and can be estimated in a single step, for example by maximum likelihood. This is in contrast to a “two-step” procedure, where the first step is to es...

state estimation is the foundation of any control and decision making in power networks. the first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. this paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual cal...

In this paper we introduce a formalism for optimal sensor parameter selection for iterative state estimation in static systems. In contrast to common approaches, where a certain metric — for example, the mean squared error between true and estimated state — is optimized during state estimation, in this work the optimality is defined in terms of reduction in uncertainty in the state estimation p...