An adaptive learning radial basis function neural network for online time series forecasting

Mazlina Mamat, and Rosalyn Porle, and Norfarariyanti Parimon, and Md Nazrul Islam, (2015) An adaptive learning radial basis function neural network for online time series forecasting. In: International Conference on Machine Learning and Signal Processing , MALSIP 2015, 12 June 2015 through 14 June 2015, Melaka, Malaysia.


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Most of the neural network based forecaster operated in offline mode, in which the neural network is trained by using the same training data repeatedly. After the neural network reaches its optimized condition, the training process stop and the neural network is ready for real forecasting. Different from this, an online time series forecasting by using an adaptive learning Radial Basis Function neural network is presented in this paper. The parameters of the Radial Basis Function neural network are updated continuously with the latest data while conducting the desired forecasting. The adaptive learning was achieved using the Exponential Weighted Recursive Least Square and Adaptive Fuzzy C-Means Clustering algorithms. The results show that the online Radial Basis Function forecaster was able to produce reliable forecasting results up to several steps ahead with high accuracy to compare with the offline Radial Basis Function forecaster.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Artificial intelligence; Clustering algorithms; Forecasting; Functions; Fuzzy clustering; Learning systems; Radial basis function networks; Time series
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: FACULTY > Faculty of Engineering
Depositing User: Unnamed user with email
Date Deposited: 05 Jan 2017 06:42
Last Modified: 10 Nov 2017 03:28

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