A Comparative Study of Activation Functions of NAR and NARX Neural Network for Long-Term Wind Speed Forecasting in Malaysia

Table 4

Supportive outcome of other studies.

Studies	ANN Model	Objective	Accuracy with tansig	Accuracy with logsig
M. Rezaeianzadeh et al. [25]	Multi-Layer Perceptron (MLP)	To predict daily outflow	( = 0.89 and RMSE = 1.69)	( = 0.80 and RMSE = 2.30)
M. Vafaeipour et al. [24]	MLP	To predict Wind velocity	(MAE = 1.48, RMSE = 1.22 and = 0.843)	(MAE = 1.48, RMSE = 1.218 and = 0.844)
M. Rezaeianzadeh et al. [22]	MLP	To forecast daily outflow	( = 0.87 and RMSE = 1.87)	( = 0.84 and RMSE = 2.1)
R. Muazu Musa et al. [23]	MLP	To identify potential archers of psychological coping skill variables	94% efficiency	84% efficiency
Aladag, and Hakan [26]	ANN	To forecast the number of outpatient visits	(RMSE = 203.06)	(RMSE = 243.28)
GSS Gomes [27]	ANN	To forecast financial time series	(MAPE=20%)	(MAPE =25.7 %)