Journal:Informatica
Volume 6, Issue 3 (1995), pp. 313–322
Abstract
The exact solution of the reliability of structures under stochastic loading is generally difficult, and various approximate methods have been developed. The most popular are the linearization method, the Monte-Carlo method and its numerous variants. In this paper new modification of the Monte-Carlo method based on asymptotical expansion is examined. Results of mathematical simulation are given.
Journal:Informatica
Volume 6, Issue 3 (1995), pp. 299–312
Abstract
In the papers (Kaminskas, 1972; Kaminskas and Nemura, 1975; Yin, 1989) the stopping rules of recursive least squares (RLS) are worked out using the ellipsoidal confidence region for the respective parameter vector of a linear dynamic system. The aim of the given paper is the development of the technique for calculating threshold intervals of respective criterions, used in a stopping rule, which are presented in Kaminskas (1972). In this connection adaptive threshold intervals based on the Cramer-Rao lower bound are proposed here. The results of numerical simulation by IBM PC/AT are given.
Journal:Informatica
Volume 6, Issue 3 (1995), pp. 289–298
Abstract
We compare two alternative ways to use the Bayesian approach in heuristic optimization. The “no-learning” way means that we optimize the randomization parameters for each problem separately. The “learning” way means that we optimize the randomization parameters for some “learning” set of problems. We use those parameters later on for a family of related problems.
We define the learning efficiency as a non-uniformity of optimal parameters while solving a set of randomly generated problems. We show that for flow-shop problems the non-uniformity of optimal parameters is significant. It means that the Bayesian learning is efficient in those problems.
Journal:Informatica
Volume 6, Issue 3 (1995), pp. 277–288
Abstract
Two-dimensional signals of physical phenomena may be inadvertently altered before recording through the system whose bandwidth is smaller than that of the signal. It is often desired to restore later such data by removing the effects of the linear system. This restoration may be accomplished by synthesizing two-dimensional (2-D) inverse filters on computers. Approximations are necessary to insure the stability of the inverse filter.
Journal:Informatica
Volume 6, Issue 3 (1995), pp. 265–276
Abstract
The results of investigation of computer programs written by school students during the 6th International Olympiad in Informatics are presented. Pascal program texts are analyzed on the lexical level. A certain relationship is indicated between program correctness and usage of some programming constructs as well as readability of the program text. The results are discussed from the standpoint of programming teaching.
Journal:Informatica
Volume 6, Issue 3 (1995), pp. 249–263
Abstract
A multiextremal problem on the synthesis of external circuit of a tunable subnanosecond pulse TRAPATT-generator was investigated using algorithms of local optimization and cluster analysis.
Journal:Informatica
Volume 6, Issue 2 (1995), pp. 233–243
Abstract
The newest version of Turbo Pascal – Turbo Pascal 7.0 – is concentrately described in comparing with optimal language for programming teaching – Standard Pascal. Data types, control structures, procedures and functions, parameters, new directions of development are classified and discussed.
Journal:Informatica
Volume 6, Issue 2 (1995), pp. 225–232
Abstract
An algorithm for the sequential analysis of multivariate data structure is presented. The algorithm is based on the sequential nonlinear mapping of L-dimensional vectors from the L-hyperspace into a lower-dimensional (two-dimensional) vectors such that the inner structure of distances among the vectors is preserved. Expressions for the sequential nonlinear mapping are obtained. The mapping error function is chosen. Theoretical minimum amount of the very beginning simultaneously mapped vectors is obtained.
Journal:Informatica
Volume 6, Issue 2 (1995), pp. 193–224
Abstract
We apply some concepts of Information-Based Complexity (IBC) to global and discrete optimization. We assume that only partial information on the objective is available. We gather this partial information by observations. We use the traditional IBC definitions and notions while defining formal aspects of the problem. We use the Bayesian framework to consider less formal aspects, such as expert knowledge and heuristics.
We extend the traditional Bayesian Approach (BA) including heuristics. We call that a Bayesian Heuristic Approach (BHA).
We discuss how to overcome the computational difficulties using parallel computing. We illustrate the theoretical concepts by three examples: by discrete problems of flow-shop scheduling and parameter grouping, and by a continuous problem of batch operations scheduling.
Journal:Informatica
Volume 6, Issue 2 (1995), pp. 181–192
Abstract
Rule-based systems are usually interpreted as a shallow expert systems realization tool. The paper analyses how the applicability of production rules can be extended using the proposed rule base structuring discipline. Its main constructions are rule grouping according to elementary aspects of investigation, and decomposition of actions. In addition, the rule cycle construction is used for discrete time simulation tasks. The proposed method is illustrated by 2 applications: the expert subsystem for a database, and the simulator of a water heater.