Biomedical subjects

A Potapov

Publications and source records attributed to A Potapov.

5 recordsLinked to original sources

PubMed2003

Convergence of reinforcement learning algorithms and acceleration of learning.

The techniques of reinforcement learning have been gaining increasing popularity recently. However, the question of their convergence rate is still open. We consider the problem of choosing the learning steps alpha(n), and their relation with discount gamma and exploration degree epsilon. Appropriate choices of these parameters may drastically influence the convergence rate of the techniques. From analytical examples, we conjecture optimal values of alpha(n) and then use numerical examples to verify our conjectures.

Journal Article↗

PubMed2002

Neural networks for estimating intrinsic dimension.

We consider the problem of feature extraction and determination of intrinsic dimensionality of observation data. One of the common approaches to this problem is to use autoassociative neural networks with a "bottleneck" projecting layer. We propose a different approach in which a neural network performs a topological mapping that creates a nonlinear lower-dimensional projection of the data. The mapping preserves relative distances of neighbors. This technique can be efficiently implemented with the help of radial basis function networks, and it is significantly faster than training an autoassotiative network. We show that the proposed technique can be used for estimating the dimension of minimal mathematical model from time series data.

Journal Article↗

PubMed2002

EFNS guideline on mild traumatic brain injury: report of an EFNS task force.

In 1999, a Task Force on Mild Traumatic Brain Injury (MTBI) was set up under the auspices of the European Federation of Neurological Societies. Its aim was to propose an acceptable uniform nomenclature for MTBI and definition of MTBI, and to develop a set of rules to guide initial management with respect to ancillary investigations, hospital admission, observation and follow-up.

Brain Injuries↗

PubMed2001

Chaotic neural control.

We consider the problem of stabilizing unstable equilibria by discrete controls (the controls take discrete values at discrete moments of time). We prove that discrete control typically creates a chaotic attractor in the vicinity of an equilibrium. Artificial neural networks with reinforcement learning are known to be able to learn such a control scheme. We consider examples of such systems, discuss some details of implementing the reinforcement learning to controlling unstable equilibria, and show that the arising dynamics is characterized by positive Lyapunov exponents, and hence is chaotic. This chaos can be observed both in the controlled system and in the activity patterns of the controller.

Journal Article↗

PubMed2001

Application of three-dimensional resonant acoustic spectroscopy method to rock and building materials.

This paper presents the experimental and theoretical results of applying resonant acoustic spectroscopy (RAS) to determine elastic parameters and losses in such consolidated granular materials as rock and building bricks. First, the theoretical aspects of the RAS method are outlined. A computer code for the rectangular and cylindrical samples was developed and tested. The results of experiments on specimens of rock and ceramic brick are then described. Finally, a modification of the previously published RUS algorithm is presented which permits a significant reduction in computing time for elongated samples.

Journal Article↗