Informatica

This work is an attempt of generalization of the simple statement about the requirements of commutation of words for the case of languages. In the paper, the necessary condition for commutation of languages are obtained, and in the prefix case the necessary and sufficient conditions are obtained. It is important to note that the considered alphabets and languages can be infinite.

The accuracy of adaptive integration algorithms for solving stiff ODE is investigated. The analysis is done by comparing the discrete and exact amplification factors of the equations. It is proved that the usage of stiffness number of the Jacobian matrix is sufficient in order to estimate the complexity of solving ODE problems by explicit integration algorithms. The complexity of implicit integration algorithms depends on the distribution of eigenvalues of the Jacobian. Results of numerical experiments are presented.

An estimation of the generalization performance of classifier is one of most important problems in pattern clasification and neural network training theory. In this paper we estimate the generalization error (mean expected probability of classification) for randomized linear zero empirical error (RLZEE) classifier which was considered by Raudys, Dičiūnas and Basalykas. Instead of “non-explicit” asymptotics of a generalization error of RLZEE classifier for centered multivariate spherically Gaussian classes proposed by Basalykas et al. (1996) we obtain an “explicit” and more simple asymptotics. We also present the numerical simulations illustrating our theoretical results and comparing them with each other and previously obtained results.

In this paper, the hexagonal approach was proposed for modeling the functioning of cerebral cortex, especially, the processes of learning and recognition of visual information. This approach is based on the real neurophysiological data of the structure and functions of cerebral cortex. Distinctive characteristic of the proposed neural network is the hexagonal arrangement of excitatory connections between neurons that enable the spreading or cloning of information on the surface of neuronal layer. Cloning of information and modification of the weight of connections between neurons are used as the basic principles for learning and recognition processes. Computer simulation of the hexagonal neural network indicated a suitability and prospectiveness of proposed approach in the creation, together with other modern concepts, of artificial neural network which will realize the most complicated processes that take place in the brain of living beings, such as short-term and long-term memory, episodic and declarative memory, recall, recognition, categorisation, thinking, and others.

This article is an introduction to the simplest mathematical model, which describes the hormone interaction during the menstrual cycle. Modifications of the mathematical model of the menstrual cycle including the mathematical model with the time delay depending on function researched and the mathematical model with the dispersed time delay are researched and described here. A numerical investigation was conducted, during which solutions for the above mentioned models were calculated. The solutions found are compared mutually and with the clinical data.

It is well known that in linear programming, the optimal values of the dual variables can be interpreted as shadow prices (marginal values) of the right-hand side coefficients. However, this is true only under nondegeneracy assumptions. Since real problems are often degenerate, the output from conventional LP software regarding such marginal information can be misleading. This paper surveys and generalizes known results in this topic and demonstrates how true shadow prices can be computed with or without modification to existing software.

This paper discusses a known-plaintext attack on a redundancy reducing cipher method which is proposed by Wayner. We also propose an extension of Wayner's redundancy reducing cipher scheme so that the security will be improved greatly.

We consider in this paper so called simple bracketed languages having special limitations. They are sometimes used for the definitions of some grammatical structures of programming languages. Generally speaking, these languages are context-free, but not deterministic context-free, i.e., they cannot be defined by deterministic push-down automata. For the simple bracketed languages having special limitations, the equivalence problem is decidable.

Methods for solving stochastic optimization problems by Monte-Carlo simulation are considered. The stoping and accuracy of the solutions is treated in a statistical manner, testing the hypothesis of optimality according to statistical criteria. A rule for adjusting the Monte-Carlo sample size is introduced to ensure the convergence and to find the solution of the stochastic optimization problem from acceptable volume of Monte-Carlo trials. The examples of application of the developed method to importance sampling and the Weber location problem are also considered.

The result of simulation of an idealized thin wet film connecting fixed points in the Euclidean plane is a length-minimizing curve. Gradually increasing the exterior pressure we are able to achieve the film configuration near to the Steiner minimal tree. This film evolution may be an interesting tool for solving the Euclidean Steiner problem, but several dead-point situations may occur for a certain location of fixed points. A continuous evolution of the film is impossible by increasing the pressure in these situations. The investigation of dead-point situations gives the ways of overcoming the difficulties of dead-point situations and continuing the film evolution by temporarily decreasing pressure.