Abstract
Functional Link Neural Network (FLNN) has been becoming as an important tool used in many applications task particularly in solving a non-linear separable problems. This is due to its modest architecture which required less tunable weights for training as compared to the standard multilayer feed forward network. The most common learning scheme for training the FLNN is a Backpropagation (BP-learning) algorithm. However, learning method by BP-learning algorithm tend to easily get trapped in local minima especially when dealing with non-linearly separable classification problems which affect the performance of FLNN. This paper discussed the implementation of modified Artificial Bee Colony with Firefly algorithm for training the FLNN network to overcome the drawback of BP-learning scheme. The aim is to introduce an alternative learning scheme that can provide a better solution for training the FLNN network for classification task.
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The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM) and Ministry of Higher Education (MOHE) Malaysia for financially supporting this research under the Fundamental Research Grant Scheme (FRGS), Vote No. 1235.
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Hassim, Y.M.M., Ghazali, R., Wahid, N. (2017). Improved Functional Link Neural Network Learning Using Modified Bee-Firefly Algorithm for Classification Task. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_8
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DOI: https://doi.org/10.1007/978-3-319-51281-5_8
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