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DCTOOL-A3
Bakule, Lubomír; Papík, Martin; Rehák, Branislav
2016 - English
DCTOOL-A3 is a documentation of Matlab routines developed for the design of decentralized control of large scale complex systems. The current beta version covers three areas as follows:\nReport 4.1 deals with the event-triggered control design for unstructured uncertain systems. Both non-quantized and quantized feedback is analyzed. The results are given in terms of linear matrix inequalities (LMIs). Logarithmic quantizer is used. Numerical example illustrates the effectiveness of the presented results.\nReport 4.2 presents a new decentralized overlapping wireless control design with a switched communication protocol. The method is applied by simulations on the 20-story building structure including the test of robustness of the methods against sensor failures and network node dropouts of a digital network.\nReport 4.3 presents the construction of a new decentralized wireless controller and a set of heuristic algorithms for evaluation of packet dropouts, sensor faults and actuator faults. The digital network operates at the standard frequency used in well-known widely-used industrial protocols. The results are tested at the Benchmark model decomposed into two disjoint substructures. The results are published. Thus, the details are omitted here.
Keywords:
event-triggered control; networked control systems; large scale complex systems
Available at various institutes of the ASCR
DCTOOL-A3
DCTOOL-A3 is a documentation of Matlab routines developed for the design of decentralized control of large scale complex systems. The current beta version covers three areas as follows:\nReport 4.1 ...
Comparison of mixture-based classification with the data-dependent pointer model for various types of components
Likhonina, Raissa; Suzdaleva, Evgenia; Nagy, Ivan
2016 - English
The presented report is devoted to the analysis of a data-dependent pointer model, whether it brings some advantages in comparison with a data-independent pointer model at simulation and estimation of components referring to different types of distribution, including categorical, uniform, exponential and state-space components for a dynamic data-dependent model, and normal components for a static data-dependent pointer model.
Keywords:
mixture-based classification; data-dependent pointer; recurisive mixture estimation
Fulltext is available at external website.
Comparison of mixture-based classification with the data-dependent pointer model for various types of components
The presented report is devoted to the analysis of a data-dependent pointer model, whether it brings some advantages in comparison with a data-independent pointer model at simulation and estimation of ...
Linear ARX and state-space model with uniform noise: computation of first and second moments
Jirsa, Ladislav
2016 - English
This report collects technical procedures used for computations of various estimates and keeps them in one place for internal purposes. The context concerns application of estimation of unknown parameters and states of linear model with uniformly distributed noise.
Keywords:
uncertainty; bounded variable; uniform noise; linear model; model identification; state estimation
Fulltext is available at external website.
Linear ARX and state-space model with uniform noise: computation of first and second moments
This report collects technical procedures used for computations of various estimates and keeps them in one place for internal purposes. The context concerns application of estimation of unknown ...
Sparse robust portfolio optimization via NLP regularizations
Branda, Martin; Červinka, Michal; Schwartz, A.
2016 - English
We deal with investment problems where we minimize a risk measure under a condition on the sparsity of the portfolio. Various risk measures are considered including Value-at-Risk and Conditional Value-at-Risk under normal distribution of returns and their robust counterparts are derived under moment conditions, all leading to nonconvex objective functions. We propose four solution approaches: a mixed-integer formulation, a relaxation of an alternative mixed-integer reformulation and two NLP regularizations. In a numerical study, we compare their computational performance on a large number of simulated instances taken from the literature. We deal with investment problems where we minimize a risk measure\nunder a condition on the sparsity of the portfolio. Various risk measures\nare considered including Value-at-Risk and Conditional Value-at-Risk\nunder normal distribution of returns and their robust counterparts are\nderived under moment conditions, all leading to nonconvex objective\nfunctions. We propose four solution approaches: a mixed-integer formulation,\na relaxation of an alternative mixed-integer reformulation and\ntwo NLP regularizations. In a numerical study, we compare their computational\nperformance on a large number of simulated instances taken\nfrom the literature.
Keywords:
Conditional Value-at-Risk; Value-at-Risk; risk measure
Fulltext is available at external website.
Sparse robust portfolio optimization via NLP regularizations
We deal with investment problems where we minimize a risk measure under a condition on the sparsity of the portfolio. Various risk measures are considered including Value-at-Risk and Conditional ...
DCTOOL-A2
Bakule, Lubomír; Papík, Martin; Rehák, Branislav
2015 - English
DCTOOL-A2 is a documentation of Matlab routines developed for the design of decentralized control of large scale complex systems in 2015.
Keywords:
decentralized control; event-triggered networked control systems; large scale complex systems
Available at various institutes of the ASCR
DCTOOL-A2
DCTOOL-A2 is a documentation of Matlab routines developed for the design of decentralized control of large scale complex systems in 2015.
Information fusion with functional Bregman divergence
Dedecius, Kamil
2015 - English
The report summarizes the basics of the Bregman divergence, its functional form and potential use for information fusion.
Keywords:
information fusion; bregman divergence; entropy
Fulltext is available at external website.
Information fusion with functional Bregman divergence
The report summarizes the basics of the Bregman divergence, its functional form and potential use for information fusion.
Recursive Estimation of High-Order Markov Chains: Approximation by Finite Mixtures
Kárný, Miroslav
2015 - English
A high-order Markov chain is a universal model of stochastic relations between discrete-valued variables. The exact estimation of its transition probabilities suers from the curse of dimensionality. It requires an excessive amount of informative observations as well as an extreme memory for storing the corresponding su cient statistic. The paper bypasses this problem by considering a rich subset of Markov-chain models, namely, mixtures of low dimensional Markov chains, possibly with external variables. It uses Bayesian approximate estimation suitable for a subsequent decision making under uncertainty. The proposed recursive (sequential, one-pass) estimator updates a product of Dirichlet probability densities (pds) used as an approximate posterior pd, projects the result back to this class of pds and applies an improved data-dependent stabilised forgetting, which counteracts the dangerous accumulation of approximation errors.
Keywords:
Markov chain; approximate parameter estimation; Bayesian recursive estimation; adaptive systems; Kullback-Leibler divergence; forgetting
Available at various institutes of the ASCR
Recursive Estimation of High-Order Markov Chains: Approximation by Finite Mixtures
A high-order Markov chain is a universal model of stochastic relations between discrete-valued variables. The exact estimation of its transition probabilities suers from the curse of dimensionality. ...
Prediction of Pedestrian Movement During The Egress Situation
Hrabák, Pavel; Ticháček, O.
2015 - English
The report summarizes the up-to-now progress in the application of the recursive estimation on the prediction of the pedestrian movement during the egress or evacuation situation. For these purposes a simple decision-making model has been introduced taking into account only the forward and sideways movement of pedestrians. Based on this model, a test simulation has been developed in order to test the applicability of the estimation tool to the stated decision-making model. Two main approaches of the decision process incorporated in the simulation are discussed and a modified version of the original model is presented. The report contains a manual to the used Matlab scripts and functions. The codes of needed m-files are incorporated as well.
Keywords:
Recursive estimation; mixture of Markov chains; pedestrian movement; egress simulation
Fulltext is available at external website.
Prediction of Pedestrian Movement During The Egress Situation
The report summarizes the up-to-now progress in the application of the recursive estimation on the prediction of the pedestrian movement during the egress or evacuation situation. For these purposes ...
Normal and uniform noise - violation of the assumption on noise distribution in model identification
Jirsa, Ladislav; Pavelková, Lenka
2015 - English
Mathematical modelling under uncertainty together with the field of applied statistics represent tools useful in many practical domains. Widely accepted assumption of normal (Gaussian) noise has created the basis for theoretical and algorithmic solutions of respective tasks. However, many continuous variables are strictly bounded and their uncertainty may have origin in various physical processes which causes a non-normal distribution of their noise. Furthermore, adaptation of algorithms based on normal model for identification of models with bounded noise can distort the estimates due to inconsistent handling of uncertainty. This report describes a study to compare results of estimation algorithms based on assumption of normal and uniform noise. Data sequences processed by the algorithms have normal noise bounded by a low limit with respect to standard deviation. We illustrate disparity between noise assumption and a true noise distribution and its influence on the quality of the estimates. It is a part of an effort to develop theory and fast algorithms for estimation with bounded noise, applicable in practice.
Keywords:
uncertainty; bounded variable; uniform noise; model identification; assumption of normal noise; estimation comparison
Fulltext is available at external website.
Normal and uniform noise - violation of the assumption on noise distribution in model identification
Mathematical modelling under uncertainty together with the field of applied statistics represent tools useful in many practical domains. Widely accepted assumption of normal (Gaussian) noise has ...
Evaluation of Kullback-Leibler Divergence
Homolová, Jitka; Kárný, Miroslav
2015 - English
Kullback-Leibler divergence is a leading measure of similarity or dissimilarity of probability distributions. This technical paper collects its analytical and numerical expressions for the broad range of distributions.
Keywords:
Kullback-Leibler divergence; cross-entropy; Bayesian decision making; Bayesian learning and approximation
Fulltext is available at external website.
Evaluation of Kullback-Leibler Divergence
Kullback-Leibler divergence is a leading measure of similarity or dissimilarity of probability distributions. This technical paper collects its analytical and numerical expressions for the broad range ...
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