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    Wyświetlanie 1-6 z 6
    Tytuł:
    Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm
    Autorzy:
    Tian, Niu
    Huang, Songwei
    He, Lifang
    Du, Lingpan
    Yang, Sheping
    Huang, Bin
    Powiązania:
    https://bibliotekanauki.pl/articles/24085898.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    fuzzy control
    contraction-expansion factor
    particle swarm optimization
    Opis:
    The grinding process in the concentrator is a part of the largest energy consumption, but also the most likely to cause a waste of resources, so the optimization of the grinding process is a very important link.The traditional fuzzy controller relies solely on the expert knowledge summary to construct control rules, which can cause significant steady-state errors in the model. In order to solve the above problem, this paper proposes an elastic variable universe fuzzy control based on Particle Swarm Optimization (PSO) algorithm. The elastic universe fuzzy control model does not need precise fuzzy rules, but only needs to input the general trend of the rules, and the division of the universe is performed by the contraction-expansionfactor. The control performance is directly related to the contraction-expansionfactor, so this article also proposes using particle swarm optimization to optimize the scaling factor to achieve the optimal value. Finally, simulation models of traditional fuzzy control and elastic universe fuzzy control of feeding system of mill were built using Python to verify the control effect. Itssimulation results show that the time of the reaction of the fuzzy control system in the elastic variable theory universe based on particle swarm optimization was shorter by 34.48% comparing to the traditional one. Elastic variable universe fuzzy control based on particle swarm optimization (PSO) effectively improved the control accuracy of the mill feeding system and improved the response speed of the system to a certain extent.
    Źródło:
    Physicochemical Problems of Mineral Processing; 2023, 59, 3; art. no. 169942
    1643-1049
    2084-4735
    Pojawia się w:
    Physicochemical Problems of Mineral Processing
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fixing Design Inconsistencies of Polymorphic Methods Using Swarm Intelligence
    Autorzy:
    George, Renu
    Samuel, Philip
    Powiązania:
    https://bibliotekanauki.pl/articles/1818478.pdf
    Data publikacji:
    2021
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    UML models
    software design inconsistency
    polymorphism
    particle swarm optimization
    Opis:
    Background: Modern industry is heavily dependent on software. The complexity of designing and developing software is a serious engineering issue. With the growing size of software systems and increase in complexity, inconsistencies arise in software design and intelligent techniques are required to detect and fix inconsistencies. Aim: Current industrial practice of manually detecting inconsistencies is time consuming, error prone and incomplete. Inconsistencies arising as a result of polymorphic object interactions are hard to trace. We propose an approach to detect and fix inconsistencies in polymorphic method invocations in sequence models. Method: A novel intelligent approach based on self regulating particle swarm optimization to solve the inconsistency during software system design is presented. Inconsistency handling is modelled as an optimization problem that uses a maximizing fitness function. The proposed approach also identifies the changes required in the design diagrams to fix the inconsistencies. Result: The method is evaluated on different software design models involving static and dynamic polymorphism and inconsistencies are detected and resolved. Conclusion: Ensuring consistency of design is highly essential to develop quality software and solves a major design issue for practitioners. In addition, our approach helps to reduce the time and cost of developing software.
    Źródło:
    e-Informatica Software Engineering Journal; 2021, 15, 1; 7--27
    1897-7979
    Pojawia się w:
    e-Informatica Software Engineering Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Estimation of composite load model parameters using improved particle swarm optimization
    Autorzy:
    Regulski, P.
    Gonzalez-Longatt, F.
    Terzija, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/410557.pdf
    Data publikacji:
    2012
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    load modeling
    parameter estimation
    particle swarm optimization (PSO)
    composite load model
    Opis:
    Power system loads are one of its crucial elements to be modeled in stability studies. However their static and dynamic characteristics are very often unknown and usually changing in time (daily, weekly, monthly and seasonal variations). Taking this into account, a measurement-based approach for determining the load characteristics seems to be the best practice, as it updates the parameters of a load model directly from the system measurements. To achieve this, a Parameter Estimation tool is required, so a common approach is to incorporate the standard Nonlinear Least Squares, or Genetic Algorithms, as a method providing more global capabilities. In this paper a new solution is proposed -an Improved Particle Swarm Optimization method. This method is an Artificial Intelligence type technique similar to Genetic Algorithms, but easier for implementation and also computationally more efficient. The paper provides results of several experiments proving that the proposed method can achieve higher accuracy and show better generalization capabilities than the Nonlinear Least Squares method. The computer simulations were carried out using a one-bus and an IEEE 39-bus test system.
    Źródło:
    Present Problems of Power System Control; 2012, 2; 41-51
    2084-2201
    Pojawia się w:
    Present Problems of Power System Control
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Quantum-inspired particle swarm optimization algorithm with performance evaluation of fused images
    Autorzy:
    Le, Z
    Xinman, Z.
    Xuebin, X
    Dong, W.
    Jie, L.
    Yang, L.
    Powiązania:
    https://bibliotekanauki.pl/articles/174501.pdf
    Data publikacji:
    2013
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    multifocus image fusion
    quantum particle swarm optimization
    perfect reconstruction
    superior speed
    Opis:
    In order to improve and accelerate the speed of image integration, an optimal and intelligent method for multi-focus image fusion is presented in this paper. Based on particle swarm optimization and quantum theory, quantum particle swarm optimization (QPSO) intelligent search strategy is introduced in salience analysis of a contrast visual masking system, combined with the segmentation technique. The superiority of QPSO is quantum parallelism. It has stronger search ability and quicker convergence speed. When compared with other classical or novel fusion methods, several metrics for image definition are exploited to evaluate the performance of all the adopted methods objectively. Experiments are performed on both artificial multi-focus images and digital camera multi-focus images. The results show that QPSO algorithm is more efficient than non-subsampled contourlet transform, genetic algorithm, binary particle swarm optimization, etc. The simulation results demonstrate that QPSO is a satisfying image fusion method with high accuracy and high speed.
    Źródło:
    Optica Applicata; 2013, 43, 4; 679-691
    0078-5466
    1899-7015
    Pojawia się w:
    Optica Applicata
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multiobjective Improved Particle Swarm Optimisation for Transmission Congestion and Voltage Profile Management using Multilevel UPFC
    Autorzy:
    Rao, Mallavolu Malleswara
    Ramadas, Geetha
    Powiązania:
    https://bibliotekanauki.pl/articles/1193696.pdf
    Data publikacji:
    2019
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    congestion
    unified power flow controller
    improved particle swarm optimization
    modular multilevel converter
    voltage profile
    Opis:
    This paper proposes a multiobjective improved particle swarm optimisation (IPSO) for placing and sizing the series modular multilevel converter-based unified power flow controller (MMC-UPFC) FACTS devices to manage the transmission congestion and voltage profile in deregulated electricity markets. The proposed multiobjective IPSO algorithm is perfect for accomplishing the close ideal distributed generation (DG) sizes while conveying smooth assembly qualities contrasted with another existing algorithm. It tends to be reasoned that voltage profile and genuine power misfortunes have generous upgrades along ideal speculation on DGs in both the test frameworks. The proposed system eliminates the congestion and the power system can be easily used to solve complex and non-linear optimisation problems in a real-time manner.
    Źródło:
    Power Electronics and Drives; 2019, 4, 39; 79-93
    2451-0262
    2543-4292
    Pojawia się w:
    Power Electronics and Drives
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Hybrid MPPT algorithm for PV systems under partially shaded conditions using a stochastic evolutionary search and a deterministic hill climbing
    Autorzy:
    Basiński, K.
    Ufnalski, B.
    Grzesiak, L. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1193446.pdf
    Data publikacji:
    2017
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    maximum power point tracking
    photovoltaic system
    hybrid part-stochastic part-deterministic search rule
    particle swarm optimization (PSO)
    partial shading
    hill climbing
    Opis:
    A hybrid maximum power point tracking method has been proposed for the photovoltaic system using a stochastic evolutionary search and a deterministic hill climbing algorithm. The proposed approach employs the particle swarm optimizer (PSO) to solve a dynamic optimization problem related to the control task in a PV system. The position of the best particle is updated by the hill climbing algorithm, and the position of the rest of the particles by the classic PSO rule. The presented method uses the re-randomization mechanism, which places five consecutive particles randomly, but in specified intervals. This mechanism helps track the maximum power point under partially shaded conditions.
    Źródło:
    Power Electronics and Drives; 2017, 2, 37/2; 49-59
    2451-0262
    2543-4292
    Pojawia się w:
    Power Electronics and Drives
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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