This paper deals with the optimal solution to the sampled-data minimum variance filtering problem for linear systems with noise in the states and in the measurements. The solution is derived in the time-domain by using a fast sampling zeroorder hold input discretization of the continuous time systems together with a lifting technique. The original sampled-data system is transformed into an equi...

Unstable dynamical systems can be viewed from a variety of perspectives. We discuss the potential of an inputoutput map associated with an unstable system to represent a bounded map from L2(R) to itself and then develop criteria for optimal reduced order approximations to the original (unstable) system with respect to an L2-induced Hilbert-Schmidt norm. Our optimality criteria extend the Meier-...

The general mathematical problem of fuzzy control is an interpolation problem: a list of fuzzy input-output data, usually provided by a list of linguistic control rules, should be realized as argument-value pairs for a suitably chosen fuzzy function. However, contrary to the usual understanding of interpolation, in the actual approaches this interpolation problem is considered as a global one: ...

In applying nonlinear model-predictive control to fast unstable systems, the main difficulty is that the optimization cannot be finished within one sampling interval. To solve this problem, a cascade-control scheme has been proposed, where input-output feedback linearization forms the inner loop and nonlinear predictive control the outer loop. Thus, the nonlinear predictive control has to handl...

This paper presents a new generalized approach to the singularity analysis of a general mechanism (arbitrary kinematic chain), considered as a non-redundant inputoutput device with equal number of inputs and outputs. The instantaneous kinematics of a mechanism is described by means of a velocity equation, explicitly including not only the input and output velocities but also the passive-joint v...

We frame max-margin learning of latent variable structured prediction models as a convex optimization problem, making use of scoring functions computed by input-output observable operator models. This learning problem can be expressed as an optimization problem involving a low-rank Hankel matrix that represents the inputoutput operator model. The direct outcome of our work is a new spectral reg...

Optimal tracking is considered for a linear time-invariant plant in data space. The proposed control strategy does not employ any traditional mathematical model such as a transfer function or a state-space equation. Instead, the plant dynamics is represented as a set of basis vectors whose elements are inputoutput data of the plant. Using this system representation, an optimal tracking problem ...

Estimating the conditional mean of an inputoutput relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of condition...

We study the probability distributions of difference propagation probabilities and inputoutput correlations for functions and block ciphers of given dimensions, for several of them for the first time. We show that these parameters have distributions that are well-studied in the field of probability such as the normal, Poisson and extreme value distributions. The results of this paper can be use...

Besides their typical classification task, Self-Organizing Maps (SOM) can be used to approximate inputoutput relations. They provide an economic way of storing the essence of past data into input/output support vector pairs. In this paper the SOLIM algorithm (Self-Organising Locally Interpolating Map) is reviewed and an extrapolation method is introduced. This framework allows finding one inver...