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Idempotent versions of Haar’s Lemma: links between comparison of discrete event systems with different state spaces and control

Mourad Ahmane, Laurent Truffet (2007)

Kybernetika

Haar's Lemma (1918) deals with the algebraic characterization of the inclusion of polyhedral sets. This Lemma has been involved many times in automatic control of linear dynamical systems via positive invariance of polyhedrons. More recently, it has been used to characterize stochastic comparison w.r.t. linear/integral ordering of Markov (reward) chains. In this paper we develop a state space oriented approach to the control of Discrete Event Systems (DES) based on the remark that most of control...

Identifiability and estimation of pharmacokinetic parameters for the ligands of the macrophage mannose receptor

Nathalie Verdiere, Lilianne Denis-Vidal, Ghislaine Joly-Blanchard, Dominique Domurado (2005)

International Journal of Applied Mathematics and Computer Science

The aim of this paper is numerical estimation of pharmacokinetic parameters of the ligands of the macrophage mannose receptor, without knowing it a priori the values of these parameters. However, it first requires a model identifiability analysis, which is done by applying an algorithm implemented in a symbolic computation language. It is shown that this step can lead to a direct numerical estimation algorithm. In this way, a first estimate is computed from noisy simulated observations without it...

Identification of a quasilinear parabolic equation from final data

Luis a. Fernández, Cecilia Pola (2001)

International Journal of Applied Mathematics and Computer Science

We study the identification of the nonlinearities A,(→)b and c appearing in the quasilinear parabolic equation y_t − div(A(y)∇y + (→)b(y)) + c(y) = u inΩ × (0,T), assuming that the solution of an associated boundary value problem is known at the terminal time, y(x,T), over a (probably small) subset of Ω, for each source term u. Our work can be divided into two parts. Firstly, the uniqueness of A,(→)b and c is proved under appropriate assumptions. Secondly, we consider a finite-dimensional optimization...

Identification of critical curves. II. Discretization and numerical realization

Jaroslav Haslinger, Václav Horák, Pekka Neittaanmäki, Kimmo Salmenjoki (1991)

Applications of Mathematics

We consider the finite element approximation of the identification problem, where one wishes to identify a curve along which a given solution of the boundary value problem possesses some specific property. We prove the convergence of FE-approximation and give some results of numerical tests.

Identification of optimal policies in Markov decision processes

Karel Sladký (2010)

Kybernetika

In this note we focus attention on identifying optimal policies and on elimination suboptimal policies minimizing optimality criteria in discrete-time Markov decision processes with finite state space and compact action set. We present unified approach to value iteration algorithms that enables to generate lower and upper bounds on optimal values, as well as on the current policy. Using the modified value iterations it is possible to eliminate suboptimal actions and to identify an optimal policy...

Image recall using a large scale generalized Brain-State-in-a-Box neural network

Cheolhwan Oh, Stanisław Żak (2005)

International Journal of Applied Mathematics and Computer Science

An image recall system using a large scale associative memory employing the generalized Brain-State-in-a-Box (gBSB) neural network model is proposed. The gBSB neural network can store binary vectors as stable equilibrium points. This property is used to store images in the gBSB memory. When a noisy image is presented as an input to the gBSB network, the gBSB net processes it to filter out the noise. The overlapping decomposition method is utilized to efficiently process images using their binary...

Imitation learning of car driving skills with decision trees and random forests

Paweł Cichosz, Łukasz Pawełczak (2014)

International Journal of Applied Mathematics and Computer Science

Machine learning is an appealing and useful approach to creating vehicle control algorithms, both for simulated and real vehicles. One common learning scenario that is often possible to apply is learning by imitation, in which the behavior of an exemplary driver provides training instances for a supervised learning algorithm. This article follows this approach in the domain of simulated car racing, using the TORCS simulator. In contrast to most prior work on imitation learning, a symbolic decision...

Immunotherapy with interleukin-2: A study based on mathematical modeling

Sandip Banerjee (2008)

International Journal of Applied Mathematics and Computer Science

The role of interleukin-2 (IL-2) in tumor dynamics is illustrated through mathematical modeling, using delay differential equations with a discrete time delay (a modified version of the Kirshner-Panetta model). Theoretical analysis gives an expression for the discrete time delay and the length of the time delay to preserve stability. Numerical analysis shows that interleukin-2 alone can cause the tumor cell population to regress.

Implementation of the MR tractography visualization kit based on the anisotropic Allen-Cahn equation

Pavel Strachota (2009)

Kybernetika

Magnetic Resonance Diffusion Tensor Imaging (MR–DTI) is a noninvasive in vivo method capable of examining the structure of human brain, providing information about the position and orientation of the neural tracts. After a short introduction to the principles of MR–DTI, this paper describes the steps of the proposed neural tract visualization technique based on the DTI data. The cornerstone of the algorithm is a texture diffusion procedure modeled mathematically by the problem for the Allen–Cahn...

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