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Functional link neural network – artificial bee colony for time series temperature prediction

Yana Mazwin, Mohmad Hassim and Ghazali, Rozaida (2013) Functional link neural network – artificial bee colony for time series temperature prediction. In: The 2013 International Conference on Computational Science and Its Applications (ICCSA 2013), 24-27 June 2013, Ho Chi Minh City.


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Higher Order Neural Networks (HONNs) have emerged as an important tool for time series prediction and have been successfully applied in many engineering and scientific problems. One of the models in HONNs is a Functional Link Neural Network (FLNN) known to be conveniently used for function approximation and can be extended for pattern recognition with faster convergence rate and lesser computational load compared to ordinary feedforward network like the Multilayer Perceptron (MLP). In training the FLNN, the mostly used algorithm is the Backpropagation (BP) learning algorithm. However, one of the crucial problems with BP learning algorithm is that it can be easily gets trapped on local minima. This paper proposed an alternative learning scheme for the FLNN to be applied on temperature forecasting by using Artificial Bee Colony (ABC) optimization algorithm. The ABC adopted in this work is known to have good exploration and exploitation capabilities in searching optimal weight especially in numerical optimization problems. The result of the prediction made by FLNN-ABC is compared with the original FLNN architecture and toward the end we found that FLNN-ABC gives better result in predicting the next-day ahead prediction.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: temperature prediction; functional link neural network; artificial bee colony algorithm
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Normajihan Abd. Rahman
Date Deposited: 11 Jul 2013 02:43
Last Modified: 21 Jan 2015 07:36
URI: http://eprints.uthm.edu.my/id/eprint/4003
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