Stabilization as a CW approximation

Parametrization and Convex Approximation Approach to Stabilization via Output Feedback

A parametric description of all static output feedback stabililizing controllers for switching diffusion systems is presented. This description is expressed in terms of coupled linear matrix equations and non-convex quadratic matrix inequalities which depend on parameter matrices similar to weight matrices in LQR theory. A convexifying approximation technique is proposed to obtain the LMIbased....

Approximation and stabilization of distributed systems by lumped systems

In this note we study two questions regarding linear time-invariant distributed systems, namely: (1) When can the given system be approximated by a lumped system? (2) When can the given system be stabilized by a lumped system? In each case we obtain a complete answer to the question in terms of the behaviour of the purely atomic parts of the stable numerator and denominator of the plant.

Approximation and stabilization of distributed sfsiems by lumped systems

Abstrocr: In this note we study two questions regarding linear time-invariant distributed systems, namely: (1) When can the given system be approximated by a lumped system? (2) When can the given system be stabilized by a lumped system? In each case we obtain a wmpkte answer to the question in terms of required for a signal to propagate down the string; but if t < T, the system is not controlla...

Dropout as a Bayesian Approximation: Appendix

We show that a neural network with arbitrary depth and non-linearities, with dropout applied before every weight layer, is mathematically equivalent to an approximation to a well known Bayesian model. This interpretation might offer an explanation to some of dropout’s key properties, such as its robustness to overfitting. Our interpretation allows us to reason about uncertainty in deep learning...

Coherent potential approximation as a voltage probe