Functional link neural network – artificial bee colony for time series temperature prediction
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 approxima...
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| Pengarang-pengarang Utama: | , |
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| Format: | Conference or Workshop Item |
| Diterbitkan: |
2013
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| Subjek-subjek: | |
| Capaian Atas Talian: | http://eprints.uthm.edu.my/4003/ http://eprints.uthm.edu.my/4003/1/Functional_Link_Neural_Network_%E2%80%93_Artificial.pdf |
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| Ringkasan: | 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. |
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