Journal:Informatica
Volume 1, Issue 2 (1990), pp. 96–109
Abstract
In the papers (Pupeikis, 1988a, b; 1989a, b, c) the problems of efficiency determination, stopping and increase of the effectiveness of asymptotically optimal recursive algorithms are considered respectively by means of estimating time delay in an object and also introducing their robust analogues, stable to outliers in observations. The aim of the given paper is the development of the robust method for a determination of the model order on the basis of determinant ratio. The three methods forming the initial moment matrices are considered. By the first method the elements of the matrix, being the corresponding values of the sample covariance and cross-covariance functions, are calculated by classical formulas. In the case of the second method the same elements are substituted by their robust analogues. The third method is based on an application of auxiliary variables. The results of numerical simulation on a computer (Table 1) indicate the advisability to apply the robust method for determining the model order in the presence of outliers.
Journal:Informatica
Volume 1, Issue 1 (1990), pp. 89–106
Abstract
This paper briefly reviews some of the recent results on the problems and algorithms for their solution in quadratic 0-1 optimization. First, the complexity of problems is discussed. Next, some exact algorithms and heuristics are mentioned. Finally, results in the analysis of the algorithms for 0-1 quadratic problems are summarized. The papers written in Russian are considered more thoroughly here.
Journal:Informatica
Volume 1, Issue 2 (1990), pp. 87–95
Abstract
The present paper considers the problem of general estimation of static model parameters and systematic measurement errors. The general estimation algorithm is based on static model linearization and on the least-squares method. The efficiency of this algorithm is illustrated by means of computer-aided digital simulation. The obtained equations and the algorithm of general estimation of static model parameters and systematic measurement errors can be applied for the solution of different practical problems. Estimatibility conditions must be satisfied in all cases.
Journal:Informatica
Volume 1, Issue 2 (1990), pp. 75–86
Abstract
This paper discusses the inversion of linear periodically time-varying (LPTV) digital filters using the idea of converting the LPTV filter to the block time-invariant filter. Explicit expressions are given to determine the inversion of LPTV filters. Controllability, observability and stability of the inversion of LPTV filters are discussed.
Journal:Informatica
Volume 1, Issue 1 (1990), pp. 71–88
Abstract
In the paper the global optimization is described from the point of an interactive software design. The interactive software that implements numeric methods and other techniques to solve global optimization problems is presented. Some problems of such a software design are formulated and discussed.
Journal:Informatica
Volume 1, Issue 1 (1990), pp. 59–70
Abstract
In this paper the problem of optimization of multivariate multimodal functions observed with random error is considered. Using the random function for a statistical model of the objective function the minimization procedure is suggested. This algorithm is convergent on a discrete set. To avoid computational difficulties, the modified algorithm is defined by substituting the parameters of minimization procedure by their estimates.
Journal:Informatica
Volume 1, Issue 1 (1990), pp. 40–58
Abstract
A new concept of an exact auxiliary function (EAF) is introduced. A function is said to be EAF, if the set of global minimizers of this function coincides with the global solution set of the initial optimization problem. Sufficient conditions for exact equivalence of the constrained minimization problem and minimization of EAF are provided. The paper presents various classes of EAF for a non linear programming problem, which has a saddle point of Lagra ge function.
Journal:Informatica
Volume 1, Issue 2 (1990), pp. 35–52
Abstract
In the paper a general approach to identification of non-linear autoregression processes in the class of parametric and non-parametric mathematical models is formulated. With the help of mathematical simulation the estimates of the processes of this class are studied: a nuclear estimate, an estimate of least squares projective estimates. Some statistical properties of these estimates are indicated.
Journal:Informatica
Volume 1, Issue 1 (1990), pp. 20–39
Abstract
In this paper we deal with the problem of extremal parameter grouping. The problem formulation, the algorithms of parameter grouping and the fields of implementation are presented. The deterministic algorithms of extremal parameter grouping often find the local maximum of the functional, characterizing the quality of a partition. The problem has been formulated as a problem of combinatorial optimization and attempted to be solved using the simulated annealing strategy. The algorithms, realizing such a strategy and devoted to the solving of the problem concerned, are proposed and investigated.