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    Wyświetlanie 1-6 z 6
    Tytuł:
    Bootstrap methods for epistemic fuzzy data
    Autorzy:
    Grzegorzewski, Przemyslaw
    Romaniuk, Maciej
    Powiązania:
    https://bibliotekanauki.pl/articles/2134054.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    bootstrap method
    fuzzy data
    fuzzy numbers
    hypotheses testing
    metoda bootstrap
    dane rozmyte
    testowanie hipotez
    Opis:
    Fuzzy numbers are often used for modeling imprecise perceptions of the real-valued observations. Such epistemic fuzzy data may cause problems in statistical reasoning and data analysis. We propose a universal nonparametric technique, called the epistemic bootstrap, which could be helpful when the existing methods do not work or do not give satisfactory results. Besides the simple epistemic bootstrap, we develop its several refinements that aim to reduce the variance in statistical inference. We also perform an extended simulation study to examine statistical properties of the approaches considered. The discussion of the results is supplemented by some hints for practical use.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 2; 285--297
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Artificial Intelligence Approaches to Fault Diagnosis for Dynamic Systems
    Autorzy:
    Patton, R. J.
    Lopez-Toribio, C. J.
    Uppal, F. J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908290.pdf
    Data publikacji:
    1999
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    metoda sztucznej inteligencji
    rozpoznanie błędu
    modelowanie rozmyte
    system rozmyty
    artificial intelligence methods
    fault diagnosis
    residual generation
    fuzzy modelling
    neuro-fuzzy systems
    Opis:
    Recent approaches to fault detection and isolation (FDI) for dynamic systems using methods of integrating quantitative and qualitative model information, based upon artificial intelligence (AI) techniques are surveyed. In this study, the use of AI methods is considered an important extension to the quantitative model-based approach for residual generation in FDI. When quantitative models are not readily available, a correctly trained artificial neural network (ANN) can be used as a non-linear dynamic model of the system. However, the neural network does not easily provide insight into model behaviour; the model is explicit rather than implicit in form. This main difficulty can be overcome using qualitative modelling or rule-based inference methods. For example, fuzzy logic can be used together with state-space models or neural networks to enhance FDI diagnostic reasoning capabilities. The paper discusses the properties of several methods of combining quantitative and qualitative system information and their practical value for fault diagnosis of real process systems.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 1999, 9, 3; 471-518
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An ε-Insensitive Approach to Fuzzy Clustering
    Autorzy:
    Łęski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908067.pdf
    Data publikacji:
    2001
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    programowanie
    metoda grupowania
    fuzzy clustering
    fuzzy c-means
    robust methods
    varepsilon-insensitivity
    fuzzy c-medians
    Opis:
    Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new varepsilon-insensitive Fuzzy C-Means (varepsilonFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). The performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) method using synthetic data with outliers and heavy-tailed, overlapped groups of the data.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2001, 11, 4; 993-1007
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Improving the Generalization Ability of Neuro-Fuzzy Systems by e-Insensitive Learning
    Autorzy:
    Łęski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908037.pdf
    Data publikacji:
    2002
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    informatyka
    fuzzy systems
    neural networks
    tolerant learning
    generalization control
    robust methods
    Opis:
    A new learning method tolerant of imprecision is introduced and used in neuro-fuzzy modelling. The proposed method makes it possible to dispose of an intrinsic inconsistency of neuro-fuzzy modelling, where zero-tolerance learning is used to obtain a fuzzy model tolerant of imprecision. This new method can be called e-insensitive learning, where, in order to fit the fuzzy model to real data, the e-insensitive loss function is used. e-insensitive learning leads to a model with minimal Vapnik-Chervonenkis dimension, which results in an improved generalization ability of this system. Another advantage of the proposed method is its robustness against outliers. This paper introduces two approaches to solving e-insensitive learning problem. The first approach leads to a quadratic programming problem with bound constraints and one linear equality constraint. The second approach leads to a problem of solving a system of linear inequalities. Two computationally efficient numerical methods for e-insensitive learning are proposed. Finally, examples are given to demonstrate the validity of the introduced methods.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2002, 12, 3; 437-447
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS
    Autorzy:
    Kumar, D. T.
    Soleimani, H.
    Kannan, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/329809.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    artificial neural network
    adaptive network based fuzzy
    inference system
    closed loop supply chain
    forecasting methods
    fuzzy neural network
    sztuczna sieć neuronowa
    system wnioskowania
    metoda prognozowania
    sieć neuronowa rozmyta
    Opis:
    Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 669-682
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Approximation of phenol concentration using novel hybrid computational intelligence methods
    Autorzy:
    Pławiak, P.
    Tadeusiewicz, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/907935.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    soft computing
    neural network
    genetic algorithm
    fuzzy system
    evolutionary neural system
    pattern recognition
    chemometrics
    przetwarzanie miękkie
    sieć neuronowa
    algorytm genetyczny
    system rozmyty
    rozpoznawanie obrazu
    chemometria
    Opis:
    This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg–Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 165-181
    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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