Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data

2005 ◽  
Vol 24 (2) ◽  
pp. 105-117 ◽  
Author(s):  
Yoshio Kajitani ◽  
Keith W. Hipel ◽  
A. Ian Mcleod
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Nonlinear Time Series ◽  
Feed Forward ◽  
Feed Forward Neural Networks

scholarly journals FASTER TRAINING USING FUSION OF ACTIVATION FUNCTIONS FOR FEED FORWARD NEURAL NETWORKS

2009 ◽  
Vol 19 (06) ◽  
pp. 437-448 ◽  
Author(s):  
MD. ASADUZZAMAN ◽  
MD. SHAHJAHAN ◽  
KAZUYUKI MURASE
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Error Function ◽  
Time Series Prediction ◽  
Back Propagation ◽  
Activation Functions ◽  
Classification Problems ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
Prediction Problems

Multilayer feed-forward neural networks are widely used based on minimization of an error function. Back propagation (BP) is a famous training method used in the multilayer networks but it often suffers from the drawback of slow convergence. To make the learning faster, we propose 'Fusion of Activation Functions' (FAF) in which different conventional activation functions (AFs) are combined to compute final activation. This has not been studied extensively yet. One of the sub goals of the paper is to check the role of linear AFs in combination. We investigate whether FAF can enable the learning to be faster. Validity of the proposed method is examined by performing simulations on challenging nine real benchmark classification and time series prediction problems. The FAF has been applied to 2-bit, 3-bit and 4-bit parity, the breast cancer, Diabetes, Heart disease, Iris, wine, Glass and Soybean classification problems. The algorithm is also tested with Mackey-Glass chaotic time series prediction problem. The algorithm is shown to work better than other AFs used independently in BP such as sigmoid (SIG), arctangent (ATAN), logarithmic (LOG).

scholarly journals An Statistical Research on Feed Forward Neural Networks for Forecasting Time Series

2012 ◽  
Vol 2 (3) ◽  
pp. 21-25 ◽  
Author(s):  
Faruk Alpaslan ◽  
Erol Eğrioğlu ◽  
Çağdaş Hakan Aladağ ◽  
Ebrucan Tiring
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Statistical Research ◽  
Feed Forward ◽  
Feed Forward Neural Networks
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