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    Wyświetlanie 1-5 z 5
    Tytuł:
    Probabilistic fuzzy approach to evaluation of logistics service effectiveness
    Autorzy:
    Rudnik, K.
    Pisz, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/406962.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    fuzzy expert systems
    fuzzy hybrid systems
    probabilistic fuzzy systems
    probability of fuzzy event
    logistics company
    logistics service provider
    logistics service
    effectiveness
    Opis:
    Logistics service providers offer a whole or partial logistics business service over a certain time period. Between such companies, the effectiveness of specific logistics services can vary. Logistics service providers seek the effective performance of logistics service. The purpose of this paper is to present a new approach for the evaluation of logistics service effectiveness, along with a specific computer system implementing the proposed approach - a sophisticated inference system, an extension of the Mamdani probabilistic fuzzy system. The paper presents specific knowledge concerning the relationships between effectiveness indicators in the form of fuzzy rules which contain marginal and conditional probabilities of fuzzy events. An inference diagram is also shown. A family of Yager’s parameterized t-norms is proposed as inference operators. It facilitates the optimization of system parameters and enables flexible adjustment of the system to empirical data. A case study was used to illustrate the new approach for the evaluation of logistics service effectiveness. The approach is demonstrated on logistics services in a logistics company. We deem the analysis of a probabilistic fuzzy knowledge base to be useful for the evaluation of effectiveness of logistics services in a logistics company over a given time period.
    Źródło:
    Management and Production Engineering Review; 2014, 5, 4; 66-75
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Hybrid Learning of Interval Type-2 Fuzzy Systems Based on Orthogonal Least Squares and Back Propagation for Manufacturing Applications
    Autorzy:
    Mendez, G.
    Hernandez, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/384517.pdf
    Data publikacji:
    2008
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    type-2 fuzzy inference systems
    type-2 neuro-fuzzy systems
    hybrid learning
    uncertain rule-based fuzzy logic systems
    Opis:
    This paper presents a novel learning methodology based on the hybrid algorithm for interval type-2 (IT2) fuzzy logic systems (FLS). Since in the literature only back-propagation method has been proposed for tuning of both antecedent and consequent parameters of type-2 fuzzy logic systems, a hybrid learning algorithm has been developed. The hybrid method uses recursive orthogonal least-squares method for tuning of consequent parameters as well as the back-propagation method for tuning of antecedent parameters. The systems were tested for three types of inputs: a) interval singleton b) interval type-1 (T1) non-singleton, c) interval type-2 non-singleton. The experimental results of the application of the hybrid interval type-2 fuzzy logic systems for scale breaker entry temperature prediction in a real hot strip mill were carried out for three different types of coils. They proved the feasibility of the systems developed here for scale breaker entry temperature prediction. Comparison with type-1 fuzzy logic systems shows that the hybrid learning interval type-2 fuzzy logic systems improve performance in scale breaker entry temperature prediction under the tested condition.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 1; 23-32
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm
    Autorzy:
    Dziwiński, Piotr
    Bartczuk, Łukasz
    Paszkowski, Józef
    Powiązania:
    https://bibliotekanauki.pl/articles/1837533.pdf
    Data publikacji:
    2020
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    hybrid methods
    Particle Swarm Optimization
    Genetic Algorithm
    fuzzy systems
    multimodal function
    Opis:
    The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 95-111
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A neural-fuzzy approach for fault diagnosis of hybrid dynamical systems: demonstration on three-tank system
    Autorzy:
    Achbi, Mohammed Said
    Kechida, Sihem
    Mhamdi, Lotfi
    Dhouibi, Hedi
    Powiązania:
    https://bibliotekanauki.pl/articles/1837950.pdf
    Data publikacji:
    2021
    Wydawca:
    Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
    Tematy:
    hybrid dynamic systems
    modelling
    residual generation
    evaluation
    monitoring
    fault diagnosis
    neural - fuzzy approach
    Opis:
    This work is part of the diagnostic field of hybrid dynamic systems (HDS) whose objective is to ensure proper operation of industrial facilities. The study is initially oriented to the modelling approach dedicated to hybrid dynamical systems (HDS). The objective is to look for an adequate model encompassing both aspects (continuous and event). Then, fault diagnosis technique is synthesised using artificial intelligence (AI) techniques. The idea is to introduce a hybrid version combining neural networks and fuzzy logic for residual generation and evaluation. The proposed approach is then validated on three tank system. The modelling and diagnosis approaches are developed using MATLAB/Simulink environment.
    Źródło:
    Acta Mechanica et Automatica; 2021, 15, 1; 1-8
    1898-4088
    2300-5319
    Pojawia się w:
    Acta Mechanica et Automatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Methodology to knowledge discovery for fault diagnosis of hybrid dynamical systems: demonstration on two tanks system
    Autorzy:
    Achbi, Mohammed Said
    Mhamdi, Lotfi
    Kechida, Sihem
    Dhouibi, Hedi
    Powiązania:
    https://bibliotekanauki.pl/articles/329522.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    hybrid dynamic systems
    generation of residues
    evaluation of residues
    monitoring
    diagnosis
    neural fuzzy systems
    hybrydowy system dynamiczny
    monitorowanie
    diagnoza
    systemy neuronowo-rozmyte
    Opis:
    The work carried out in this article concerns on the implementation off a diagnostic procedure for hybrid dynamic systems (HDS) whose objective is to guarantee the proper functioning of industrial installations. In this context, the main contributions of this work are summarized into three parts: The first part is oriented to the modeling approach dedicated to HDS. The aim is to find an adequate model combining both aspects (continuous and discrete dynamics). The use of Neuro-fuzzy networks makes it possible to build a model of the system and to follow all the modes without it being necessary to identify or discern them. The second part concerns the synthesis of a fault diagnostic technique based on a fuzzy inference system. A Neuro-Fuzzy network based is used for residual generation, while for the residual evaluation, a fuzzy reasoning model is used which can mainly introduce heuristic information into the analysis scheme and takes the appropriate decision regarding the actual behaviour of the process. The proposed approach is successfully applied to monitoring faults of a non-linear three-tank system and the results confirm the effectiveness of this approach.
    Źródło:
    Diagnostyka; 2020, 21, 4; 115-122
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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