The full text of this article

 

Physical time series prediction using Recurrent Pi-Sigma Neural Networks
by Abir Jaafar Hussain, Panos Liatsis, Hissam Tawfik, Atulya K. Nagar, Dhiya Al-Jumeily
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 1, No. 1, 2008

 

Abstract: This paper presents a new type of recurrent neural network, called the Recurrent Pi-Sigma Neural Network (RPSN) and its application to physical time series prediction. The network is constructed of two layers, the sigma and the pi unit layers. The recurrent pi-sigma network calculates the product sum of the weighted inputs and passes the results to a non-linear transfer function. The output of the network is the feedback to its input. The performance of the network is tested in non-linear and non-stationary physical signal prediction. Two popular time series, the mean value of the AE index and the number of sunspots, are used in our studies. The simulation results showed an average improvement in the Signal to Noise Ratio (SNR) of 1.85 dB over the feedforward pi-sigma neural networks.

Online publication date: Fri, 14-Nov-2008

 

is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

 
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

 
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Artificial Intelligence and Soft Computing (IJAISC):
Login with your Inderscience username and password:

 

    Username:        Password:         

Forgotten your password?


 
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

 
If you still need assistance, please email subs@inderscience.com