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Wyszukujesz frazę "BP neural network" wg kryterium: Temat


Wyświetlanie 1-6 z 6
Tytuł:
Research on the risk classification of cruise ship fires based on an attention-BP neural network
Autorzy:
Xiong, Zhenghua
Xiang, Bo
Chen, Ye
Chen, Bin
Powiązania:
https://bibliotekanauki.pl/articles/32912853.pdf
Data publikacji:
2022
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
cruise fire
simulation modeling
ensemble learning
BP neural network
Opis:
Due to the relatively closed environment, complex internal structure, and difficult evacuation of personnel, it is more difficult to prevent ship fires than land fires. In this paper, taking the large cruise ship as the research object, the physical model of a cruise cabin fire is established through PyroSim software, and the safety indexes such as smoke temperature, CO concentration, and visibility are numerically simulated. An Attention-BP neural network model is designed for realizing the intelligent identification of a cabin fire and dividing the risk level, which integrates the diagnosis results of multiple neural network models through the self-Attention mechanism and adaptively distributes the weight of each BP neural network model. The proposed model can provide decision-making reference for subsequent fire-fighting measures and personnel evacuation. Experimental results show that the proposed Attention-BP neural network model can effectively realize the early warning of the fire risk level. Compared with other machine learning algorithms, it has the highest stability and accuracy and reduces the uncertainty of early cabin fire warning.
Źródło:
Polish Maritime Research; 2022, 3; 61-68
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Data-driven temporal-spatial model for the prediction of AQI in Nanjin
Autorzy:
Zhao, Xuan
Song, Meichen
Liu, Anqi
Wang, Yiming
Wang, Tong
Cao, Jinde
Powiązania:
https://bibliotekanauki.pl/articles/1837414.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
air quality prediction
k-Nearest Neighbor
BP neural network
non-monitoring stations
Opis:
Air quality data prediction in urban area is of great significance to control air pollution and protect the public health. The prediction of the air quality in the monitoring station is well studied in existing researches. However, air-quality-monitor stations are insufficient in most cities and the air quality varies from one place to another dramatically due to complex factors. A novel model is established in this paper to estimate and predict the Air Quality Index (AQI) of the areas without monitoring stations in Nanjing. The proposed model predicts AQI in a non-monitoring area both in temporal dimension and in spatial dimension respectively. The temporal dimension model is presented at first based on the enhanced k-Nearest Neighbor (KNN) algorithm to predict the AQI values among monitoring stations, the acceptability of the results achieves 92% for one-hour prediction. Meanwhile, in order to forecast the evolution of air quality in the spatial dimension, the method is utilized with the help of Back Propagation neural network (BP), which considers geographical distance. Furthermore, to improve the accuracy and adaptability of the spatial model, the similarity of topological structure is introduced. Especially, the temporal-spatial model is built and its adaptability is tested on a specific non-monitoring site, Jiulonghu Campus of Southeast University. The result demonstrates that the acceptability achieves 73.8% on average. The current paper provides strong evidence suggesting that the proposed non-parametric and data-driven approach for air quality forecasting provides promising results.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 4; 255-270
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
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
Tytuł:
Estimation of Shipment Size in Seaborne Iron Ore Trade
Autorzy:
Zhou, X.
Hu, Q.
Powiązania:
https://bibliotekanauki.pl/articles/117393.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
cargo handling
shipment size
estimation of shipment size
seaborne iron ore trade
AIS Data
Maritime Mobile Service Identify (MMSI)
BP Neural Network
iron ore
Opis:
Shipment size is unavailable and important in AIS-based trade volume estimates. A method of shipment size estimates based on AIS (Automatic Identification System) data and BP neural network is proposed. The ship's length, width, designed draught, current draught and deadweight ton are input parameters, the actual shipment size of the ship is output value, and the BP neural network is trained to estimate the actual shipment size of the iron ore carriers. Then, the AIS data is used to calculate the iron ore trade volume in 2018. Compared with customs data, the annual error of import volume of China is less than 0.5%. The result shows that the proposed method is accurate and practical.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2019, 13, 4; 791-796
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
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
    Wyświetlanie 1-6 z 6

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