Do evolutionary algorithms indeed require random numbers? Extended study

An inherent part of evolutionary algorithms, that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes. In this participation, we discuss whether are random processes really needed in evolutionary algorithms. We use n periodic deterministic processes instead of random number generators and compare performance of evolutionary algorithms powered by those processes and by pseudo-random number generators. Deterministic processes used in this participation are based on deterministic chaos and are used to generate periodical series with different length. Results presented here are numerical demonstration rather than mathematical proofs. We propose that a certain class of deterministic processes can be used instead of random number generators without lowering of evolutionary algorithms performance. © Springer International Publishing Switzerland 2013

Partitioning networks into cliques: a randomized heuristic approach

In the context of community detection in social networks, the term community can be grounded in the strict way that simply everybody should know each other within the community. We consider the corresponding community detection problem. We search for a partitioning of a network into the minimum number of non-overlapping cliques, such that the cliques cover all vertices. This problem is called the clique covering problem (CCP) and is one of the classical NP-hard problems. For CCP, we propose a randomized heuristic approach. To construct a high quality solution to CCP, we present an iterated greedy (IG) algorithm. IG can also be combined with a heuristic used to determine how far the algorithm is from the optimum in the worst case. Randomized local search (RLS) for maximum independent set was proposed to find such a bound. The experimental results of IG and the bounds obtained by RLS indicate that IG is a very suitable technique for solving CCP in real-world graphs. In addition, we summarize our basic rigorous results, which were developed for analysis of IG and understanding of its behavior on several relevant graph classes

Zpráva o výsledcích vědecko-výzkumné a vývojové činnosti na VŠB-TU Ostrava za rok 2013 : určeno pro zasedání Vědecké rady VŠB-TUO dne 7. 3. 2014

Ostrav

ARTIFICIAL NEURAL NETWORK FOR MODELS OF HUMAN OPERATOR

This paper presents a new approach to mental functions modeling with the use of artificial neural networks. The artificial neural networks seems to be a promising method for the modeling of a human operator because the architecture of the ANN is directly inspired by the biological neuron. On the other hand, the classical paradigms of artificial neural networks are not suitable because they simplify too much the real processes in biological neural network. The search for a compromise between the complexity of biological neural network and the practical feasibility of the artificial network led to a new learning algorithm. This algorithm is based on the classical multilayered neural network; however, the learning rule is different. The neurons are updating their parameters in a way that is similar to real biological processes. The basic idea is that the neurons are competing for resources and the criterion to decide which neuron will survive is the usefulness of the neuron to the whole neural network. The neuron is not using "teacher" or any kind of superior system, the neuron receives only the information that is present in the biological system. The learning process can be seen as searching of some equilibrium point that is equal to a state with maximal importance of the neuron for the neural network. This position can change if the environment changes. The name of this type of learning, the homeostatic artificial neural network, originates from this idea, as it is similar to the process of homeostasis known in any living cell. The simulation results suggest that this type of learning can be useful also in other tasks of artificial learning and recognition

Modifications of optimization algorithms applied in multivariable predictive control

Non-linear optimization, particularly quadratic programming (QP), is a mathematical method which is widely applicable in model predictive control (MPC). It is significantly important if constraints of variables are considered in MPC and the optimization task is then computationally demanding. The result of the optimization is a vector of future increments of a manipulated variable. The first element of this vector is applied in the next sampling period of MPC in the framework of a receding horizon strategy. In practical realization of a multivariable MPC, the optimization is characterized by higher computational complexity. Therefore, reduction of the computational complexity of the optimization methods has been widely researched. Besides the generally used numerical Hildreth’s method of QP, a possible suitable modification is based on precomputing operations proposed by Wang, L. This general optimization strategy is further modified. Two modifications, which could be applied separately each, were interconnected in this paper. The first modification was published previously; however, its application can be more efficient in connection with the second proposed approach, which modifies precomputing operations. Decreasing of the computational complexity of the optimization by using of the proposal is discussed and analyzed by measurements of floating point operations and control quality criterions using hypotheses tests – paired T-test and Wilcoxon test. © 2018, World Scientific and Engineering Academy and Society. All rights reserved

Zpráva o výsledcích vědecko-výzkumné a vývojové činnosti na VŠB-TU Ostrava za rok 2014 : určeno pro zasedání Vědecké rady VŠB-TUO dne 6. 3. 2015

Ostrav

Supporting the life cycle of complex technical object on the basis of predictive analytics

Application of predictive analytics in design, production and exploitation to achieve efficiency of life cycle of complex technical systems is discussed. Predictive model for life cycle information support of microsatellite propulsion system on the basis of system of neural networks is suggested. The predictive model can solve tasks of estimation of fuel consumption, diagnostics and possible failures detection for the small propulsion system

Intellectual technologies and decision support systems for the control of the economic and financial processes

Запропоновано комп'ютерна система підтримки прийняття рішень, основними завданнями якої є побудова адаптивної моделі і прогнозування різних типів процесів, які розвиваються в соціально-економічних системах під впливом фундаментальних структурних змін. Складність і актуальність розв'язуваної проблеми полягає в необхідності забезпечити прийнятні якісні прогнози фінансових і економічних показників для коротких вибірок даних, коли використання ретроспективних даних неможливо або суттєво обмежена. Розробка СППР заснована на принципах системного аналізу, тобто можливості обліку деяких стохастичних та інформаційних невизначеностей, формування альтернатив для моделей і прогнозів і відстеження правильності обчислювальних процедур на всіх етапах обробки даних. Реалізована модульна архітектура, яка забезпечує можливість подальшого розширення і модифікації функціональних можливостей системи за допомогою нових методів прогнозування та оцінки параметрів. Крім того, пропонована система, завдяки модульній архітектурі, може бути поліпшена за рахунок використання програмного забезпечення різних виробників без будь-яких додаткових структурних змін. Висока якість кінцевого результату досягається завдяки належному відстеження обчислювальних процедур на всіх етапах обробки даних в ході обчислювальних експериментів: попередня обробка даних, побудова моделі і оцінка прогнозів. Відстеження виконується з відповідними наборами статистичних параметрів якості. Наведено приклад оцінки фінансового ризику в сфері страхування та споживання електроенергії з точки зору енергозбереження. Наведені приклади показують, що розроблена система має хороші перспективи для практичного використання. Передбачається, що система буде універсальною і знайде своє застосування в якості додаткового інструменту для підтримки прийняття рішень при розробці стратегій для компаній і підприємств різних типів.A computer-based decision support system is proposed the basic tasks of which are adaptive model constructing and forecasting of various types of processes that are developing in socio-economic systems under the influence of fundamental structural changes. The complexity and urgency of the solvable problem is the need to provide acceptable quality forecasts of financial and economic indicators for short data samples, when the usage of retrospective data is impossible or significantly limited. The DSS development is based on the system analysis principles, i.e. the possibility for taking into consideration of some stochastic and information uncertainties, forming alternatives for models and forecasts, and tracking of the computing procedures correctness during all stages of data processing. A modular architecture is implemented that provides a possibility for the further enhancement and modification of the system functional possibilities with new forecasting and parameter estimation techniques. In addition, the proposed system, thanks to the modular architecture, can be improved by using the software of different vendors without any additional structural changes. A high quality of the final result is achieved thanks to appropriate tracking of the computing procedures at all stages of data processing during computational experiments: preliminary data processing, model constructing, and forecasts estimation. The tracking is performed with appropriate sets of statistical quality parameters. The example is given for estimation of financial risk in the insurance sphere and the electricity consumption in terms of energy saving. The examples solved show that the system developed has good perspectives for practical use. It is supposed that the system will be universal and find its applications as an extra tool for support of decision making when developing the strategies for companies and enterprises of various types.Предложена компьютерная система поддержки принятия решений, основными задачами которой являются построение адаптивной модели и прогнозирование различных типов процессов, которые развиваются в социально-экономических системах под воздействием фундаментальных структурных изменений. Сложность и актуальность решаемой проблемы заключается в необходимости обеспечить приемлемые качественные прогнозы финансовых и экономических показателей для коротких выборок данных, когда использование ретроспективных данных невозможно или существенно ограничено. Разработка СППР основана на принципах системного анализа, то есть возможности учета некоторых стохастических и информационных неопределенностей, формирования альтернатив для моделей и прогнозов и отслеживания правильности вычислительных процедур на всех этапах обработки данных. Реализована модульная архитектура, которая обеспечивает возможность дальнейшего расширения и модификации функциональных возможностей системы с помощью новых методов прогнозирования и оценки параметров. Кроме того, предлагаемая система, благодаря модульной архитектуре, может быть улучшена за счет использования программного обеспечения разных производителей без каких-либо дополнительных структурных изменений. Высокое качество конечного результата достигается благодаря надлежащему отслеживанию вычислительных процедур на всех этапах обработки данных в ходе вычислительных экспериментов: предварительная обработка данных, построение модели и оценка прогнозов. Отслеживание выполняется с соответствующими наборами статистических параметров качества. Приведен пример оценки финансового риска в сфере страхования и потребления электроэнергии с точки зрения энергосбережения. Решенные примеры показывают, что разработанная система имеет хорошие перспективы для практического использования. Предполагается, что система будет универсальной и найдет свое применение в качестве дополнительного инструмента для поддержки принятия решений при разработке стратегий для компаний и предприятий различных типов

Structured film-viewing preferences and practices : a quantitative analysis of hierarchies in screen and content selection amongst young people in Flanders

Aleit Veenstra, Philippe Meers and Daniel Biltereyst address a specific segment of a typical small-market audience—Flemish youth film viewers. Their study “Structured Film Viewing Preferences and Practices: A Quantitative Analysis of Hierarchies in Screen and Content Selection among Young People in Flanders” deals with one of the symptomatic problems of the era of convergent audiences, the multiplication of screens used for domestic consumption of audiovisual content. Building an intriguing empirical design, Veenstra and her colleagues aim to identify patterns of screen selection and their relation to the perceived value of Hollywood, European and domestic Flemish films. Their conclusion is that there are well-articulated hierarchies applied by the audience members in the selection of both film titles and reception screens and that, to put it simply, in the case of screens, size matters