Artificial Neural Networks In Prediction And Predictive Control

Artificial neural networks, prediction, model predictiv

Adaptive Control Of Isothermal Reactor With Complex Reaction

The role and importance of the simulation rises with the increasing speed of computers and simulation tools provided nowadays. Safety and less money and time demands gives a computer simulation big advantage over the experiments on a real system or its model. The paper deals with simulation of an adaptive control on a nonlinear system represented by a Continuous Stirred Tank Reactor (CSTR). This system is mathematically described by a set of Ordinary Differential Equations (ODE) which are first solved numerically to obtain steady-state and dynamic behaviour of the system. The adaptive control is based on the recursive identification of an External Linear Model (ELM) as a representation of the originally nonlinear system. The polynomial approach together with the pole-placement method gives sufficient control results although the system has negative control properties

Innovative inorganic binder systems for the production of cores for non-ferrous metal alloys reflecting the product quality requirements

The aim of this study is the evaluation of the parameters of core mixtures using different binder systems with regard to the collapsibility of cores after casting and the resulting product quality of castings reflecting surface requirements based on non-ferrous alloys. The research compares organically bonded core mixtures based on phenol-formaldehyde resins for the production of cores with the shell molding (resin coated sand), currently used in the production of aluminum alloy castings in the Brembo Czech s.r.o., and mixtures using innovative inorganic binder systems based on geopolymers; GEOPOL(R) W. The aim of the research is to compare the advantages and disadvantages of these binder systems in order to evaluate the potential of inorganically bonded mixtures to replace organically bonded mixtures, which would lead to a significant reduction in the environmental impacts of industrial production of castings.Web of Science115art. no. 73

Návrh měřící tratě s axiálním lutnovým ventilátorem

Prezenční361 - Katedra energetikyNeuveden

Optimization of the Matching Criteria Between the ATLAS and AFP Detectors at CERN

This thesis is a part of CERN’s ongoing research of the search for axion-like-particle (ALP). It consists of a software to modify existing data to include calculated uncertainties on photon and proton measurements and a software that calculates the difference between correlated and uncorrelated data. Also included is a study on matching criteria for proton-proton interactions with two detected photons. The study discusses the performance of three different matching criteria regarding the kinematics of the di-photon system with at least one detected proton. The created software fulfills all stated requirements, including execution time requirement. The main discovery from the physics point of view is that the interactions studied in the data are caused by pile-up background, thus they are random interactions with no indication of a specific physics process, which is consistent with previous simulations

Investment strategies in the long run with proportional transaction costs and a HARA utility function

We consider an agent who invests in a stock and a money market in order to maximize the asymptotic behaviour of expected utility of the portfolio market price in the presence of proportional transaction costs. The assumption that the portfolio market price is a geometric Brownian motion and the restriction to a utility function with hyperbolic absolute risk aversion (HARA) enable us to evaluate interval investment strategies. It is shown that the optimal interval strategy is also optimal among a wide family of strategies and that it is optimal also in a time changed model in the case of logarithmic utility.Portfolio choice, Utility functions, Trading strategies, Portfolio optimization, Transaction costs,

Integralni reprezentace v konvexni a stochasticke analyze.

Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi

Comparison of Two Identification Models Used in Adaptive Control of Continuous-Stirred Tank Reactor

The goal of this paper is to compare two identification methods – continuous-time and discrete-time. The continuous-time identification model is more accurate but not very suitable for on-line identification. This disadvantage was overcome with the use of differential filters. On the other hand, discrete-time identification model has is more suitable for identification but less accurate. Compromise can be found in the delta model as a special type of the discrete-time model parameters of which are related to the sampling period. The adaptive approach is based on the choice of the External Linear Model, parameters of which are identified recursively which satisfies the adaptivity of this system. Proposed control strategy was applied on the mathematical model of the Continuous Stirred-Tank reactor as a typical nonlinear lumped-parameters system used in the industry

DRYADdostalJEcol2018

Biomass data were collected in the garden pot experiment from five community types in April 2017. Explanation to column headings: "Target species" = one of 49 species used in the experiment; "Competitor species" = one of two competitor species (Arrhenatherum elatius or Origanum vulgare) used in the experiment; "Community type" = see Fig. 1 in "Dostál P. et al. (2018) Linking species abundance and overyielding from experimental communities with niche and fitness characteristics. Journal or Ecology" for details how five community types were constructed; "#Target individuals planted"= number of target species individuals planted per pot; "#Competitor individuals planted"= number of target species individuals planted per pot; "Inoculum" = identity of species from the first (conditioning) phase serving as a source of soil inoculum in the second experimental phase (HET indicates heterospecific incululum prepared by mixing first-phase soils of all 49 target species); "Target biomass (g, per pot)" = total target species biomass estimated in a pot; "Target biomass (mg, per 1 planted ind.)" = biomass per single planted individual of target species; "Competitor biomass (g, per pot)" = total competitor species biomass estimated in a pot; "Competitor biomass (mg, per 1 planted ind.)" = biomass per single planted individual of competitor species