Abstract
This paper assessed the implementation of trigonometric basis function as input enhancement for Functional Link Neural Network (FLNN) trained with Ant Lion Optimizer (ALO) learning algorithm. The previous work of FLNN trained with ALO model used the tensor model to introduce nonlinearities in its inputs features enhancements. One of the major concerns of using the tensor model is that when FLNN has large number of input features, the network may contain many higher-order terms which could lead to combinatorial explosion in the number of weights as the order of the network becomes excessively high. To avoid this, trigonometric basis function in implemented in the model. The result on classification performance made by FLNN with trigonometric basis architecture and FLNN with tensor model architecture both trained with ALO were carried out. From the result achieved, the implementation of the FLNN with trigonometric basis trained with ALO performs the classification task quite well and yields better accuracy on the unseen data.
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Acknowledgments
This research was supported by Ministry of Higher Education (MOHE) through Fundamental Research Grant Scheme (FRGS/1/2020/ICT02/UTHM/02/1).
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Hassim, Y.M.M., Ghazali, R. (2022). The Effect of Trigonometric Basis Function on Functional Link Neural Network with Ant Lion Optimizer. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_24
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DOI: https://doi.org/10.1007/978-3-031-00828-3_24
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