Training functional link neural network with ant lion optimizer

Mohmad Hassim, Yana Mazwin and Ghazali, Rozaida (2020) Training functional link neural network with ant lion optimizer. In: International Conference on Soft Computing and Data Mining, 22-23 January 2020, Langkawi, Malaysia.

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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

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: functional link neural network; learning algorithm;ant lion optimizer.
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA76.75-76.765 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Information Security
Depositing User: Mrs. Normardiana Mardi
Date Deposited: 02 Nov 2021 03:14
Last Modified: 02 Nov 2021 03:14

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