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    Wyświetlanie 1-4 z 4
    Tytuł:
    Intelligent control algorithm for ship dynamic positioning
    Autorzy:
    Meng, W.
    Sheng, L. H.
    Qing, M.
    Rong, B. G.
    Powiązania:
    https://bibliotekanauki.pl/articles/229337.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    dynamic positioning
    fuzzy control
    neural network control
    BP algorithm
    Opis:
    Ship motion in the sea is a complex nonlinear kinematics. The hydrodynamic coefficients of ship model are very difficult to accurately determine. Establishing accurate mathematical model of ship motion is difficult because of changing random factors in the marine environment. Aiming at seeking a method of control to realize ship positioning, intelligent control algorithms are adopt utilizing operator's experience. Fuzzy controller and the neural network controller are respectively designed. Through simulations and experiments, intelligent control algorithm can deal with the complex nonlinear motion, and has good robustness. The ship dynamic positioning system with neural network control has high positioning accuracy and performance.
    Źródło:
    Archives of Control Sciences; 2014, 24, 4; 479-497
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multi-level health degree analysis of vehicle transmission system based on PSO-BP neural network data fusion
    Autorzy:
    Wu, Jianpeng
    Cui, Jiahao
    Shu, Yuechao
    Wang, Yuxin
    Chen, Ruihan
    Wang, Liyong
    Powiązania:
    https://bibliotekanauki.pl/articles/24200805.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    vehicle transmission system
    data fusion
    PSO-BP algorithm
    health degree
    Opis:
    In order to realize the evaluation of the vehicle transmission system health degree, a prediction model by multi-level data fusion method is established in this paper. The prediction model applies PSO(Particle Swarm Optimization)-BP(Back Propagation) neural network algorithm, calculates the whole machine health degree and each module respective weights from the test data. On this basis, it analyzes the error between the model calculated health degree and theoretical health degree. Then the research verifies the validity and prediction model accuracy. The health degree which is obtained by the single module feature parameters fusion, and the vehicle transmission system health degree is investigated, which is less effective compared to the three-level fusions. After that, by analyzing the vehicle transmission system multi-parameter feature weights, it is found that the mechanical module accounted for the largest damage rate, and the three modules influenced the vehicle transmission system health degree in the order of mechanical module, hydraulic module, and electric control module. The study has played a guiding role in the health management of complex equipment.
    Źródło:
    Eksploatacja i Niezawodność; 2023, 25, 1; art. no. 4
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction and optimization of tower mill grinding power consumption based on GA-BP neural network
    Autorzy:
    Wang, Ziyang
    Hou, Ying
    Sobhy, Ahmed
    Powiązania:
    https://bibliotekanauki.pl/articles/27323660.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    tower mill
    grinding power consumption
    energy saving
    genetic algorithm
    BP neural network
    Opis:
    Grinding is commonly responsible for the liberation of valuable minerals from host rocks but can entail high costs in terms of energy and medium consumption, but a tower mill is a unique power-saving grinding machine over traditional mills. In a tower mill, many operating parameters affect the grinding performance, such as the amount of slurry with a known solid concentration, screw mixer speed, medium filling rate, material-ball ratio, and medium properties. Thus, 25 groups of grinding tests were conducted to establish the relationship between the grinding power consumption and operating parameters. The prediction model was established based on the backpropagation “BP” neural network, further optimized by the genetic algorithm GA to ensure the accuracy of the model, and verified. The test results show that the relative error of the predicted and actual values of the backpropagation “BP” neural network prediction model within 3% was reduced to within 2% by conducting the generic algorithm backpropagation “GA-BP” neural network. The optimum grinding power consumption of 41.069 kWh/t was obtained at the predicted operating parameters of 66.49% grinding concentration, 301.86 r/min screw speed, 20.47% medium filling rate, 96.61% medium ratio, and 0.1394 material-ball ratio. The verifying laboratory test at the optimum conditions, produced a grinding power consumption of 41.85 kWh/t with a relative error of 1.87%, showing the feasibility of using the genetic algorithm and BP neural network to optimize the grinding power consumption of the tower mill.
    Źródło:
    Physicochemical Problems of Mineral Processing; 2023, 59, 6; art. no. 172096
    1643-1049
    2084-4735
    Pojawia się w:
    Physicochemical Problems of Mineral Processing
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation
    Autorzy:
    He, Ping
    Dong, Jie
    Wu, Xiaopeng
    Yun, Lei
    Yang, Hua
    Powiązania:
    https://bibliotekanauki.pl/articles/27309934.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    BP neural network
    photovoltaic power generation
    PSO–GWO model
    PSO–GWO–BP prediction model
    particle swarm optimization
    gray wolf optimization
    back propagation
    standard grey wolf algorithm
    Opis:
    At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.
    Źródło:
    Archives of Electrical Engineering; 2023, 72, 3; 613--628
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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