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An approach to improve functional link neural network training using modified artificial bee colony for classification task

Mohmad Hassim, Yana Mazwin and Ghazali, Rozaida (2012) An approach to improve functional link neural network training using modified artificial bee colony for classification task. Asia-Pacific Journal of Information Technology and Multimedia, 2 (2). pp. 63-71. ISSN 22892192

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Abstract

Classification is one of the most frequent studies in the area of Artificial Neural Network (ANNs). One of the best known types of ANNs is the Multilayer Perceptron (MLP). However, MLP usually requires a rather large amount of available measures in order to achieve good classification accuracy. To overcome this, a Functional Link Neural Networks (FLNN), which has single layer of trainable connection weight is used. The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. In this paper, a modified Artificial Bee Colony (mABC) is used to recover the BP drawbacks. With modifications on the employed bee’s exploitation phase, the implementation of the mABC as a learning scheme for FLNN has given a better accuracy result for the classification tasks.

Item Type: Article
Uncontrolled Keywords: Classification; functional link neural network; artificial bee colony algorithm
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 13 Aug 2018 03:37
Last Modified: 13 Aug 2018 03:37
URI: http://eprints.uthm.edu.my/id/eprint/9423
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