An improved functional link neural network learning using artificial bee colony optimisation for time series prediction Online publication date: Sat, 28-Jun-2014
by Yana Mazwin Mohmad Hassim; Rozaida Ghazali
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 8, No. 4, 2013
Abstract: Functional link neural network (FLNN) has emerged as an important tool used for function approximation and IT application on physical time series prediction. The standard learning scheme used for the training of FLNN is the Backpropagation (BP) learning algorithm. However, one of the crucial problems with BP learning algorithm is it tends to easily get trapped on local minima and thus affect the performance of FLNN. This paper proposed an alternative learning scheme for FLNN by using an artificial bee colony (ABC) optimisation algorithm as an attempt to overcome this problem. The performance of FLNN-ABC model is measured based on the prediction task on the physical time series data. The result of the prediction made by FLNN-ABC is compared with the original FLNN architecture and towards the end we found that FLNN-ABC gives better result in predicting the next-day ahead prediction.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and 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
Our Blog 
Follow us on Twitter 
Visit us on Facebook