Informatica

This paper is devoted to the consideration of the evolution of the non-migrating limited panmiction population taking into account the size, sex and age structure, pregnancy and females restoration period after delivery. The unique solvability of this model and the condition for the population to vanishe is obtained.

The paper describes the use of adaptive and non-periodic sampling in different fields of System Theory and Control. The review is organized in a very comprehensive way and it presents results of the last thirty years about the problem of signal applications using as main tool adaptive sampling schemes including results is the improvement of the transient behaviors. Also, related results are presented about the use of non-periodic sampling in compensation as an alternative design to the well-known frequency domain methods and about the choice of the sampling points in order to improve the transmission of measuring and/or rounding errors towards the results when studying the properties of dynamic systems such as controllability, observability and identifiability.

We consider a stochastic algorithm of optimization in the presented paper. We deal here with the average results of a “mixture” of the deterministics heuristics algorithm and uniform random search. We define the optimal “mixture”.

Weak approximation methods for initial value problem for the parabolic equation are considered. We propose some simple tests to investigate the quality of RNG used in Monte-Carlo simulations. Numerical examples are given to illustrate the application of stochastic approximation methods.

There exist two principally different approaches to design the classification rule. In classical (parametric) approach one parametrizes conditional density functions of the pattern classes. In a second (nonparametric) approach one parametrizes a type of the discriminant function and minimizes an empirical classification error to find unknown coefficients of the discriminant function. There is a number of asymptotic expansions for an expected probability of misclassification of parametric classifiers. Error bounds exist for nonparametric classifiers so far. In this paper an exact analytical expression for the expected error EPN of nonparametric linear zero empirical error classifier is derived for a case when the distributions of pattern classes are spherically Gaussian. The asymptotic expansion of EPN is obtained for a case when both the number of learning patterns N and their, dimensionality p increase infinitely. The tables for exact and approximate expected errors as functions of N, dimensionality p and the distance δ between pattern classes are presented and compared with the expected error of the Fisher's linear classifier and indicate that the minimum empirical error classifier can be used even in cases where dimensionality exceeds the number of learning examples.

The goal of this work is to describe the underlying theoretical and algorithmic basis of a MATLAB-based software developed by the authors. The software is intended for investigation of time series (signals) which can be modeled as the sum of real-valued quasipolynomials plus white noise. With the help of the software described, one can compute the expressions of the Cramér-Rao lower bound on the covariance matrix of the estimation error of unbiased estimates of damping factors and frequencies of quasipolynomials and to obtain estimates of these parameters using three versions of Prony method. Using this software, one can generate various models of quasipolynomials, obtain plots of their poles with respect to the unit circle, compute and plot 2σ-bounds (where σ is given by the CRB formula) around each pole, and also pole estimates obtained in each realization. Results of numerical experiments are presented.

The equations describing the evolution of migrating populations composed of two-sexes are derived taking into account the size, age structure, panmiction mating of sexes, pregnancy of females, possible abortions as well as the females organism restoration periods after abortions and delivery. In partial case, which neglects females organism restoration period, the unique solvability of the model is proved and the condition for population to vanish is obtained.

This paper presents a direct adaptive, control algorithm, based on a σ-modification rule, which is robust respect to additive and multiplicative plant unmodelled dynamics for plants involving both internal (i.e., in the state) and external (i.e., in the output or input) known point delays. Several adaptive controller structures are given and analyzed for the case of plants with unknown parameters while being assumed that the nominal plant is of known order and relative order. The parametrized parts of two of the controller structures involve delays while those of the two remaining controllers are delay-free. However, auxiliary compensating signals which weight the plant input and output integrals are incorporated in all the controller structures for stabilization purposes. It is proved that, if the unmodelled dynamics is sufficiently small at low frequencies, then the adaptive algorithm guarantees boundedness of all the signals in the closed-loop system.

In the papers (Kaminskas, 1973; Kaminskas and Nemura, 1975) the stopping rule of recursive least squares (RLS) is worked out using the length of the confidence interval for the respective current meaning of the true output signal of a linear dynamic system. The aim of the given paper is the development of techniques for calculating threshold intervals of respective criteria, used in such a stopping rule. In this connection adaptive threshold intervals based on the Cramer-Rao lower bound according to Pupeikis (1995) are proposed here, too. The results of numerical simulation by IBM PC/AT are given.

In this paper, we propose to present the direct form recursive digital filter as a state space filter. Then, we apply a look-ahead technique and derive a pipelined equation for block output computation. In addition, we study the stability and multiplication complexity of the proposed pipelined-block implementation and compare with complexities of other methods. An algorithm is derived for the iterative computation of pipelined-block matrices.