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Training Functional Link Neural Network with Ant Lion Optimizer

  • Yana Mazwin Mohmad HassimEmail author
  • Rozaida Ghazali
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 978)

Abstract

Functional Link Neural Network (FLNN) has becoming as an important tool used in machine learning due to its modest architecture. FLNN requires less tunable weights for training as compared to the standard multilayer feed forward network such as Multilayer Perceptron (MLP). Since FLNN uses Backpropagation algorithm as the standard learning algorithm, the method however prone to get trapped in local minima which affect its performance. This paper proposed the implementation of Ant Lion Algorithm as learning algorithm to train the FLNN for classification tasks. The Ant Lion Optimizer (ALO) is the metaheuristic optimization algorithm that mimics the hunting mechanism of antlions in nature. The result of the classification made by FLNN-ALO is compared with the standard FLNN model to examine whether the ALO learning algorithm is capable of training the FLNN network and improve its performance. From the result achieved, it can be seen that the implementation of the proposed learning algorithm for FLNN performs the classification task quite well and yields better accuracy on the unseen data.

Keywords

Functional link neural network Learning algorithm Ant lion optimizer 

Notes

Acknowledgments

The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM) for supporting this research.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn Malaysia (UTHM)Batu PahatMalaysia

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