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Wyszukujesz frazę "wielowarstwowe sieci neuronowe" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Routine forecasting of the daily profiles of hourly water distribution in cities. An effectiveness analysis
Autorzy:
Cieżak, W.
Cieżak, J.
Powiązania:
https://bibliotekanauki.pl/articles/206970.pdf
Data publikacji:
2015
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
forecasting
multilayer neural networks
neural networks
time series
water supply systems
water distribution
prognozowanie
wielowarstwowe sieci neuronowe
sieci neuronowe
szereg czasowy
wodociągi
dystrybucja wody
Opis:
Sample results have been oresented of verifying three groups of methods of forecasting the time series of short-duration water distributions in city water grids. The analysis covered: ARIMA class models, the time series exponential smoothing methods and artificial neural networks. Since chronological sequences of observations from the immediate past were analyzed, the adopted models did not take any external variables into account. The forecasting errors in the case of multilayer perceptron neural networks were found to be comparable or smaller than the errors of prediction by the ARIMA class models and by the methods of the exponential smoothing of time series.
Źródło:
Environment Protection Engineering; 2015, 41, 2; 179-186
0324-8828
Pojawia się w:
Environment Protection Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forward and inverse kinematics solution of a robotic manipulator using a multilayer feedforward neural network
Autorzy:
Sharkawy, Abdel-Nasser
Powiązania:
https://bibliotekanauki.pl/articles/2201647.pdf
Data publikacji:
2022
Wydawca:
Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:
multilayer neural network
feedforward neural network
forward kinematics
inverse kinematics
2-DOF planar robot
Levenberg-Marquardt algorithm
generated data
sieci neuronowe
sieci neuronowe jednokierunkowe
sieci neuronowe wielowarstwowe
kinematyka prosta
kinematyka odwrotna
algorytm Levenberga-Marquardta
generowanie danych
Opis:
In this paper, a multilayer feedforward neural network (MLFFNN) is proposed for solving the problem of the forward and inverse kinematics of a robotic manipulator. For the forward kinematics solution, two cases are presented. The first case is that one MLFFNN is designed and trained to find solely the position of the robot end-effector. In the second case, another MLFFNN is designed and trained to find both the position and the orientation of the robot end-effector. Both MLFFNNs are designed considering the joints’ positions as the inputs. For the inverse kinematics solution, a MLFFNN is designed and trained to find the joints’ positions considering the position and the orientation of the robot end-effector as the inputs. For training any of the proposed MLFFNNs, data is generated in MATLAB using two different cases. The first case is that data is generated assuming an incremental motion of the robot’s joints, whereas the second case is that data is obtained with a real robot considering a sinusoidal joint motion. The MLFFNN training is executed using the Levenberg-Marquardt algorithm. This method is designed to be used and generalized to any DOF manipulator, particularly more complex robots such as 6-DOF and 7-DOF robots. However, for simplicity, this is applied in this paper using a 2-DOF planar robot. The results show that the approximation error between the desired output and the estimated one by the MLFFNN is very low and it is approximately equal to zero. In other words, the MLFFNN is efficient enough to solve the problem of the forward and inverse kinematics, regardless of the joint motion type.
Źródło:
Journal of Mechanical and Energy Engineering; 2022, 6, 2; 1--17
2544-0780
2544-1671
Pojawia się w:
Journal of Mechanical and Energy Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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