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Wyszukujesz frazę "Bayesian optimization" wg kryterium: Temat

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    Wyświetlanie 1-6 z 6
    Tytuł:
    Bayesian optimization for solving high-frequency passive component design problems
    Autorzy:
    Baranowski, Michal
    Fotyga, Grzegorz
    Lamecki, Adam
    Mrozowski, Michal
    Powiązania:
    https://bibliotekanauki.pl/articles/2173688.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    high-frequency design
    machine learning
    Bayesian optimization
    optymalizacja bayesowska
    konstrukcja o wysokiej częstotliwości
    nauczanie maszynowe
    Opis:
    In this paper, the performance of the Bayesian Optimization (BO) technique applied to various problems of microwave engineering is studied. Bayesian optimization is a novel, non-deterministic, global optimization scheme that uses machine learning to solve complex optimization problems. However, each new optimization scheme needs to be evaluated to find its best application niche, as there is no universal technique that suits all problems. Here, BO was applied to different types of microwave and antenna engineering problems, including matching circuit design, multiband antenna and antenna array design, or microwave filter design. Since each of the presented problems has a different nature and characteristics such as different scales (i.e. number of design variables), we try to address the question about the generality of BO and identify the problem areas for which the technique is or is not recommended.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 4; art. no. e141595
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Evolutionary algorithm for learning Bayesian structures from data
    Autorzy:
    Kozłowski, M.
    Wierzchoń, S. T.
    Powiązania:
    https://bibliotekanauki.pl/articles/1986916.pdf
    Data publikacji:
    2002
    Wydawca:
    Politechnika Gdańska
    Tematy:
    Bayesian networks
    structure learning
    evolutionary algorithm
    discrete optimization
    Opis:
    In this paper we report an evolutionary approach to learning Bayesian networks from data. We explain reasons, which advocate such a non-deterministic approach. We analyze weaknesses of previous works and come to conclusion that we should operate in the search space native for the problem i.e. in the space of directed acyclic graphs instead of standard space of binary strings. This requires adaptation of evolutionary methodology into very specific needs. We propose quite new data representation and implementation of generalized genetic operators and then we present an efficient algorithm capable of learning complex networks without additional assumptions. We discuss results obtained with this algorithm. The approach presented in this paper can be extended with the possibility to absorb some suggestions from experts or obtained by means of data preprocessing.
    Źródło:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2002, 6, 3; 509-521
    1428-6394
    Pojawia się w:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Maintenance Evaluation and Optimization of a Multi-State System Based on a Dynamic Bayesian Network
    Autorzy:
    Dahia, Zakaria
    Bellaouar, Ahmed
    Dron, Jean-Paul
    Powiązania:
    https://bibliotekanauki.pl/articles/2023959.pdf
    Data publikacji:
    2021-09
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    multi-state system
    dynamic Bayesian network
    reliability
    availability
    maintenance optimization
    Opis:
    Nowadays, the main challenge in maintenance is to establish a dynamic maintenance strategy to significantly track and improve the performance measures of multi-state systems in terms of production, quality, security and even the environment. This paper presents a quantitative approach based on Dynamic Bayesian Network (DBN) to model and evaluate the maintenance of multi-state system and their functional dependencies. According to transition relationships between the system states modeled by the Markov process, a DBN model is established. The objective is to evaluate the reliability and the availability of the system with taking into account the impact of maintenance strategies (perfect repair and imperfect repair). Using the proposed approach, the dynamic probabilities of system states can be determined and the subsystems contributing to system failure can also be identified. A practical application is demonstrated by a case study of a blower system. Through the result of the diagnostic inference, to improve the performances of the blower, the critical components C, F, W, and P should be given more attention. The results indicate also that the perfect repair strategy can improve significantly the performances of the blower, while the imperfect repair strategy cannot degrade the performances in comparison to the perfect repair strategy. These results show the effectiveness of this approach in the context of a predictive evaluation process and in providing the opportunity to evaluate the impact of the choices made on the future measurement of systems performances. Finally, through diagnostic analysis, intervention management and maintenance planning are managed efficiently and optimally.
    Źródło:
    Management and Production Engineering Review; 2021, 13, 3; 3-14
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Falcon optimization algorithm for bayesian network structure learning
    Autorzy:
    Kareem, Shahab Wahhab
    Okur, Mehmet Cudi
    Powiązania:
    https://bibliotekanauki.pl/articles/2097968.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    Bayesian network
    global search
    falcon optimization algorithm
    structure learning
    search and score
    Opis:
    In machine-learning, some of the helpful scientific models during the production of a structure of knowledge are Bayesian networks. They can draw the relationships of probabilistic dependency among many variables. The score and search method is a tool that is used as a strategy for learning the structure of a Bayesian network. The authors apply the falcon optimization algorithm (FOA) to the learning structure of a Bayesian network. This paper has employed reversing, deleting, moving, and inserting to obtain the FOA for approaching the optimal solution of a structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is associated with pigeon-inspired optimization, greedy search, and simulated annealing that apply the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques by utilizing several benchmark data sets. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values).
    Źródło:
    Computer Science; 2021, 22 (4); 553--569
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Learning the naive Bayes classifier with optimization models
    Autorzy:
    Taheri, S.
    Mammadov, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/908351.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    Bayesian networks
    naive Bayes classifier
    optimization
    discretization
    sieci bayesowskie
    naiwny klasyfikator Bayesa
    optymalizacja
    dyskretyzacja
    Opis:
    Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization models for the naive Bayes classifier where both class probabilities and conditional probabilities are considered as variables. The values of these variables are found by solving the corresponding optimization problems. Numerical experiments are conducted on several real world binary classification data sets, where continuous features are discretized by applying three different methods. The performances of these models are compared with the naive Bayes classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. The obtained results demonstrate that the proposed models can significantly improve the performance of the naive Bayes classifier, yet at the same time maintain its simple structure.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2013, 23, 4; 787-795
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Evolutionary computation based on Bayesian classifiers
    Autorzy:
    Miquelez, T.
    Bengoetxea, E.
    Larranaga, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/907630.pdf
    Data publikacji:
    2004
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    rozumowanie probabilistyczne
    obliczenia ewolucyjne
    sieć Bayesa
    estymacja algorytmu dystrybucji
    hybrid soft computing
    probabilistic reasoning
    evolutionary computing
    classification
    optimization
    Bayesian networks
    estimation of distribution algorithms
    Opis:
    Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). This paper contributes to the further development of this discipline by introducing a new evolutionary computation method based on the learning and later simulation of a Bayesian classifier in every generation. In the method we propose, at each iteration the selected group of individuals of the population is divided into different classes depending on their respective fitness value. Afterwards, a Bayesian classifier---either naive Bayes, seminaive Bayes, tree augmented naive Bayes or a similar one---is learned to model the corresponding supervised classification problem. The simulation of the latter Bayesian classifier provides individuals that form the next generation. Experimental results are presented to compare the performance of this new method with different types of EDAs and GAs. The problems chosen for this purpose are combinatorial optimization problems which are commonly used in the literature.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 335-349
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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