Training functional link neural network with ant lion optimizer
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 Backprop...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/3414/ http://eprints.uthm.edu.my/3414/2/KP%202020%20%2870%29.pdf |
| Summary: | 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 |
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