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    Wyświetlanie 1-4 z 4
    Tytuł:
    Extraction of fuzzy rules using deterministic annealing integrated with ε-insensitive learning
    Autorzy:
    Czabański, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/908395.pdf
    Data publikacji:
    2006
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    system rozmyty
    sieć neuronowa
    sieć neuronowa rozmyta
    ekstrakcja reguł
    fuzzy systems
    neural networks
    neuro-fuzzy systems
    rules extraction
    deterministic annealing
    Opis:
    A new method of parameter estimation for an artificial neural network inference system based on a logical interpretation of fuzzy if-then rules (ANBLIR) is presented. The novelty of the learning algorithm consists in the application of a deterministic annealing method integrated with ε-insensitive learning. In order to decrease the computational burden of the learning procedure, a deterministic annealing method with a “freezing” phase and ε-insensitive learning by solving a system of linear inequalities are applied. This method yields an improved neuro-fuzzy modeling quality in the sense of an increase in the generalization ability and robustness to outliers. To show the advantages of the proposed algorithm, two examples of its application concerning benchmark problems of identification and prediction are considered.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2006, 16, 3; 357-372
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A modified particle swarm optimization procedure for triggering fuzzy flip-flop neural networks
    Autorzy:
    Kowalski, Piotr A.
    Słoczyński, Tomasz
    Powiązania:
    https://bibliotekanauki.pl/articles/2055168.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    fuzzy neural network
    fuzzy flip-flop neuron
    particle swarm optimization
    training procedure
    sieć neuronowa rozmyta
    optymalizacja rojem cząstek
    procedura szkoleniowa
    Opis:
    The aim of the presented study is to investigate the application of an optimization algorithm based on swarm intelligence to the configuration of a fuzzy flip-flop neural network. Research on solving this problem consists of the following stages. The first one is to analyze the impact of the basic internal parameters of the neural network and the particle swarm optimization (PSO) algorithm. Subsequently, some modifications to the PSO algorithm are investigated. Approximations of trigonometric functions are then adopted as the main task to be performed by the neural network. As a result of the numerical verification of the problem, a set of rules are developed that can be helpful in constructing a fuzzy flip-flop type neural network. The obtained results of the computations significantly simplify the structure of the neural network in relation to similar conditions known from the literature.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 577--586
    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:
    An Expert System Coupled With a Hierarchical Structure of Fuzzy Neural Networks for Fault Diagnosis
    Autorzy:
    Calado, J. M. F.
    Costa, I. S.
    Powiązania:
    https://bibliotekanauki.pl/articles/908283.pdf
    Data publikacji:
    1999
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    rozpoznanie błędu
    wykrywanie błędu
    system ekspertowy
    sieć neuronowa rozmyta
    fault diagnosis
    fault detection
    fault isolation
    shallow knowledge
    deep knowledge
    expert system
    fuzzy neural network
    abrupt faults
    incipient faults
    Opis:
    An on-line fault diagnosis system, designed to be robust to the normal transient behaviour of the process, is described. The overall system consists of an expert system cascade with a hierarchical structure of fuzzy neural networks, corresponding to a multi-stage fault detection and isolation system. The fault detection is performed through the expert system by means of fault detection heuristic rules, generated from deep and shallow knowledge of the process under consideration. If a fault is detected, the hierarchical structure of fuzzy neural networks starts and it performs the fault isolation task. The structure of this diagnosis system was designed to allow for the diagnosis of single and multiple simultaneous abrupt and incipient faults from only single abrupt fault symptoms. Also, it combines the advantages of both fuzzy reasoning and neural networks learning capacity. A continuous binary distillation column has been used as a test bed of the current approach. Single, double and triple simultaneous abrupt faults, as well as incipient faults, have been considered. The preliminary results obtained show a good accuracy, even in the case of multiple faults.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 1999, 9, 3; 667-687
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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