Sarežgītu sistēmu vadības loģiskie pamati

Mācību līdzeklis paredzēts studentiem, kuri apguvē studiju programmas “Diskrēta matemātika” un “Transporta vadības diskrētie modeli”. Grāmata satur ievadu loģiskas algebras funkcijās (vai loģikas funkcijās), kuras veido teorētiskus pamatus skaitļošanas tehnikai, informatīvai tehnoloģijai un automatizētai vadībai. Tā ietver sevī 12 nodaļas, tajā skaitā: loģikas funkciju definīcijas un īpašības, to pierakstīšanas normālformās, normālformu optimizācija, loģikas laika funkcijas, loģiskie tīkli un shēmas. Daudzie praktiskie piemēri ilustrē atziņas: sistēmas drošums, datu bāzes modeļi, datora darba principiāla shēma, pielietošanas vadības uzdevumos. Katra nodaļa beidzas ar datorprogrammām Mathcad valodā un uzdevumiem, kuri ir paredzēti pastāvīgam atrisinājumam

Transporta sistēmu optimizācijas metodes. 1. dala. Lineārā programmēšana

Mācību līdzeklis paredzēts studentiem, kuri apguvē studiju programmas “Transporta procesu optimizācija” un “Transporta plūsmas”. Grāmata satur ievadu lineārajā programmēšanā un dažādos piemērus tās pielietošanas transporta procesu plānošanai un organizācijai. Tā ietver sevī 16 nodaļas, tajā skaitā lineāras programmēšanas un transporta uzdevuma pamatjēdzieni, galvenie teorētiskie rezultāti un pamat procedūras. Pielikumi satur datoru programmas Mathcad valodā, kuras realizē apskatāmas procedūras

Conceptual Description of Latvia's Transport System Development Optimization and Forecasting Complex Model

A conceptual description of complex model for Latvia’s transport system development forecasting and optimization is represented. As the main criterion of optimization, Latvia’s inhabitant 100 km out-of-town communication weighted average time and cost expenses have been taken into consideration. Separate blocks of the model are enumerated (Information, Calculation, Forecasting, Economical evaluation, Optimization). For the main blocks (Calculation, Forecasting and Optimization) basic solved tasks and used mathematical models and algorithms are enumerated, literature sources are given. The analytical description of the inhabitants’ allocation on the territory of Latvia (given by A.Kashurin) is used essentially. A nonlinear regression model for a forecasting of passenger flows between various areas of Latvia has been described. The gradient method is used for unknown parameter estimation.Task solution order of priority is described. It is noted that computation procedure is iterative. The iterations are ended when experts accept a suggested variant

On a Simple Method of Multivariate Distribution Estimation Defined by Copula

A simple method of multivariate distribution estimation defined by copula is suppose

Resampling Based Non-Parametric Statistical Inferences about Distribution, Moments and Quantiles of Order Statistics

A subject of consideration is the n-th order statistic U(n,k) for independent and identical distributed continuous random variables X1 , X2, ... , Xk. The problem of interest is an estimation of distribution, moments and quantiles, also construction of interval estimators based on the sample X1 , X2, ... , Xn of size n > k. The resampling method is used for this aim. The paper contains analytical analysis of this approach efficiency

On Nonlinear Regression Model for Correspondence Matrix of Transport Network

A nonlinear regression model for a forecasting of passenger flow between various geographical points (towns) is described. Unknown parameters are estimated using aggregated data when only the information on the departed from each town passenger’s number is available. As the estimation efficiency criterion the weighted sum of residual squares is used

Компьютеризация преподавания дисциплин прикладной математики

В данном сообщении мы хотели бы обсудить некоторые положения, являющиеся, на наш взгляд, основными причинами компьютеризации преподавания математики. При этом мы будем иметь в виду, что указанная компьютеризация осуществляется путём использования некоторого математического пакета компьютерных программ. Мы ориентируемся на пакет MATHCAD

On Robustness of Resampling-Estimators for Linear Regression Models

The application of the resampling-approach to the linear regression model analysis is considered. The mean square estimators are known to be the best estimators in case of regression model without nuisance observations. Alternatively, in the case of model with nuisance observations called disturbed model the classical approach gives bad, biased estimators. The considered numerical example shows that the resampling-approach gives comparably good results for disturbed models

On Robustness of Resampling Estimators for Linear Regression Models

The application of the resampling-approach to the linear regression model analysis is considered. The mean square estimators are known to be the best estimators in case of regression model without nuisance observations. Alternatively, in the case of model with nuisance observations called disturbed model the classical approach gives bad, biased estimators. The considered numerical example shows that the resampling-approach gives comparably good results for disturbed models