Abstract
This paper discusses Neural Networks as predictor for analyzing of transmission angle of slider-crank mechanism. There are different types of neural network algorithms obtained by using chain rules. The neural network is a feedforward neural network. On the other hand, the slider-crank mechanism is a modified mechanism by using an additional link between connecting rod and crank pin. Through extensive simulations, these neural network models are shown to be effective for prediction and analyzing of a modified slider-crank mechanism’s transmission angle.
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Abbreviations
- TDC:
-
Top dead center
- BDC:
-
Bottom dead center
- μ :
-
Transmission angle
- l :
-
Crank arm length
- l :
-
Connecting rod length
- p :
-
Radius of pinion gear
- e :
-
Distance of eccentricity
- θ :
-
Crank rotation angle
- φi:
-
Weighted sum
- q(.):
-
Non-linear dynamic function
- ydi :
-
i th desired outputs
- yi :
-
i th outputs of the network
- Θ :
-
Unknown parameters
- w:
-
Network weight
- η :
-
Learning rate
- α :
-
Momentum constant
- δ mi :
-
Error signal of thei th neuron in them thlayer
- b m−1i :
-
Bias input to neuroni in layerm-1.
- ni :
-
Number of neurons in the input layer
- nj :
-
Number of neurons in the hidden layer
- nk :
-
Number of neurons in the output layer
- N:
-
Training numbers
- RMSE:
-
Root Mean Square Error
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Yildirim, Ş., Erkaya, S., Su, S. et al. Design of neural networks model for transmission angle of a modified mechanism. J Mech Sci Technol 19, 1875–1884 (2005). https://doi.org/10.1007/BF02984266
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DOI: https://doi.org/10.1007/BF02984266