Modelling the influence of pH, fluoride, and caffeine on the corrosion resistance of TiMo alloys by artificial neural networks developed with differential evolution algorithm

2015 ◽  
Vol 66 (9) ◽  
pp. 982-994 ◽  
Author(s):  
D. Mareci ◽  
E.-N. Dragoi ◽  
G. Bolat ◽  
R. Chelariu ◽  
D. Gordin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Corrosion Resistance ◽  
Artificial Neural Networks ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Evolution Algorithm ◽  
Artificial Neural ◽  
Influence Of Ph

scholarly journals The Training Of Multiplicative Neuron Model Based Artificial Neural Networks With Differential Evolution Algorithm For Forecasting

2016 ◽  
Vol 6 (1) ◽  
pp. 5-11 ◽  
Author(s):  
Eren Bas
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Differential Evolution ◽  
Neuron Model ◽  
Differential Evolution Algorithm ◽  
Series Data ◽  
Feed Forward ◽  
Evolution Algorithm ◽  
Artificial Neural

Abstract In recent years, artificial neural networks have been commonly used for time series forecasting by researchers from various fields. There are some types of artificial neural networks and feed forward artificial neural networks model is one of them. Although feed forward artificial neural networks gives successful forecasting results they have a basic problem. This problem is architecture selection problem. In order to eliminate this problem, Yadav et al. (2007) proposed multiplicative neuron model artificial neural network. In this study, differential evolution algorithm is proposed for the training of multiplicative neuron model for forecasting. The proposed method is applied to two well-known different real world time series data.

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