Huge data sets from the teletraffic industryexhibit many non-standard characteristics such as heavy tails andlong range dependence. The paper is devoted to the nonparametrickernel estimation of the univariate probability density functionby dependent time series data. The knowledge of univariate marginsis especially important for the bivariate analysis of data, e.g.,TCP-flow sizes and durations. Such analysis allows to evaluate thedistribution of the maximal throughput. It can be used forintrusion detection, too. It is known that the bias of a kernelestimate is the same for independent and dependent data. However,the variance is larger for the dependent case and depends on thecorrelation structure of the data. It is the idea to select such abandwidth of the kernel estimate to reduce the $MSE$ in the caseof dependent data. It is shown that the $MSE$ can converge forshort- or long-range dependent data at the close rate as the datawould under the assumption of independence.

A problem of constructing stable recursive algorithms to be used within a broad class of identification and learning problems is considered. An approach is presented leading to obtaining strongly consistent algorithms. Both cases of multi and single input/multi and single output (MIMO, MISO, SISO) linear stochastic dynamic systems are involved. Thus obtained, the recursive algorithms do not involve inversion of the performance index Hessian and are stable to sampled data, in contrast to conventional recursive schemes. Simulation examples are presented, which confirm practical efficiency of the approach.

В докладе рассмотрены методические принципы координации генерирующих блоков в крупных энергообъединениях.

The present paper deals with statements of a recently published book of the “Nauka” PublishingHouse (Moscow, 2005) on regularity of appearance of the Fibonacci golden mean as a quantitativecharacteristic in the social and economical sphere and necessity to follow it under increasing managementefficiency. The present paper proposes a negative answer to the question on necessity of following thegolden mean under managing social and economical systems. Such an answer is justified by formulation(as continued as required) a series of questions, motivated by real practice, which may not be scientificallyanswered, by a corresponding analysis, and by referring to serious relevant literature sources notmentioning the considered golden mean method.

In this paper we study the localization problem of compact invariant sets of nonlinear systems. Methods of a solution of this problem are discussed and a new method is proposed which is based on using symmetrical prolongations and the first-order extremum condition. Our approach is applied to the system modeling the Rayleigh–Bénard convection for which the symmetrical prolongation with the Lorenz system has been constructed.